A relationship between the transient structure in the monomeric state and the aggregation propensities of α-synuclein and β-synuclein

Conformational Plasticity in α-Synuclein and How Crowded Environment Modulates It

A 140-residue intrinsically disordered protein (IDP), α-synuclein (αS), is known to adopt conformations that are vastly plastic and susceptible to environmental cues and crowders. However, the inherently heterogeneous nature of αS has precluded a clear demarcation of its monomeric precursor between aggregation-prone and functionally relevant aggregation-resistant states and how a crowded environment could modulate their mutual dynamic equilibrium. Here, we identify an optimal set of distinct metastable states of αS in aqueous media by dissecting a 73 μs-long molecular dynamics ensemble via building a comprehensive Markov state model (MSM). Notably, the most populated metastable state corroborates with the dimension obtained from PRE-NMR studies of αS monomer, and it undergoes kinetic transition at diverse time scales with a weakly populated random-coil-like ensemble and a globular protein-like state. However, subjecting αS to a crowded environment results in a nonmonotonic compaction of these metastable conformations, thereby skewing the ensemble by either introducing new tertiary contacts or by reinforcing the innate contacts. The early stage of dimerization process is found to be considerably expedited in the presence of crowders, albeit promoting nonspecific interactions. Together with this, using an extensively sampled ensemble of αS, this exposition demonstrates that crowded environments can potentially modulate the conformational preferences of IDP that can either promote or inhibit aggregation events.

Molecular dynamics simulations of an α-synuclein NAC domain fragment with a ff14IDPSFF IDP-specific force field suggest β-sheet intermediate states of fibrillation

For the discovery of treatments against synucleinopathies, it is necessary to unravel and fully understand the mechanism of fibrillation of proteins involved. Among them, α-synuclein (αS) plays a key role in the development of these diseases through its aggregation into oligomers found in Lewy bodies. However, its structural disorder as an intrinsically disordered protein (IDP) makes its characterization by experimental techniques arduously difficult. Atomistic simulations aim to provide insights into this blank canvas and, fortunately, some studies have already suggested promising mechanisms. Still, it is urgent to consider the IDP features in simulations, so recently a lot of force fields designed to deal with IDPs have been developed. In this study, we have carried out a total of 12 μs simulations of an αS core fragment using a popular ff14SB AMBER force field and the ff14IDPSFF variation that includes a grid-based energy correction map (CMAP) method. The predicted chemical shifts from the simulations and those measured from the αS protein in the NMR solution indicate that ff14IDPSFF reproduces the experimental data more accurately. Moreover, structural analysis exhibits opposite trends between secondary structure propensities. The ff14SB force field preserves the α-helices found in the micelle-bound αS structure, which is used as an initial conformation, while ff14IDPSFF stands out with increased structural disorder and the formation of β-sheets, which suggests that the IDP-specific force field can capture more suitable conformations representing the possible intermediate states of the fibrillation process.

Kisspeptin-10 Rescues Cholinergic Differentiated SHSY-5Y Cells from α-Synuclein-Induced Toxicity In Vitro

The neuropathological substrate of dementia with Lewy bodies (DLB) is defined by the inextricable cross-seeding accretion of amyloid-β (Aβ) and α-synuclein (α-syn)-laden deposits in cholinergic neurons. The recent revelation that neuropeptide kisspeptin-10 (KP-10) is able to mitigate Aβ toxicity via an extracellular binding mechanism may provide a new horizon for innovative drug design endeavors. Considering the sequence similarities between α-syn’s non-amyloid-β component (NAC) and Aβ’s C-terminus, we hypothesized that KP-10 would enhance cholinergic neuronal resistance against α-syn’s deleterious consequences through preferential binding. Here, human cholinergic SH-SY5Y cells were transiently transformed to upsurge the mRNA expression of α-syn while α-syn-mediated cholinergic toxicity was quantified utilizing a standardized viability-based assay. Remarkably, the E46K mutant α-syn displayed elevated α-syn mRNA levels, which subsequently induced more cellular toxicity compared with the wild-type α-syn in choline acetyltransferase (ChAT)-positive cholinergic neurons. Treatment with a high concentration of KP-10 (10 µM) further decreased cholinergic cell viability, while low concentrations of KP-10 (0.01–1 µM) substantially suppressed wild-type and E46K mutant α-syn-mediated toxicity. Correlating with the in vitro observations are approximations from in silico algorithms, which inferred that KP-10 binds favorably to the C-terminal residues of wild-type and E46K mutant α-syn with CDOCKER energy scores of −118.049 kcal/mol and −114.869 kcal/mol, respectively. Over the course of 50 ns simulation time, explicit-solvent molecular dynamics conjointly revealed that the docked complexes were relatively stable despite small-scale fluctuations upon assembly. Taken together, our findings insinuate that KP-10 may serve as a novel therapeutic scaffold with far-reaching implications for the conceptualization of α-syn-based treatments.

α-Synuclein phosphorylation at serine 129 occurs after initial protein deposition and inhibits seeded fibril formation and toxicity

SignificanceConverging evidence points to the build-up of phosphorylated α-synuclein (α-syn) at residue serine 129 (pS129) in Lewy body disease, suggesting its central role in the regulation of α-syn aggregation and neuronal degeneration. However, a comprehensive understanding of the role of α-syn phosphorylation at pS129 in α-synuclenopathies pathogenesis is still lacking. Herein, we study the phosphorylation incidence and its effect on α-syn aggregation propensity and cellular toxicity. Collectively, our data suggest that pS129 occurred subsequent to initial α-syn aggregation, lessened aggregation propensity, and attenuated cytotoxicity through diverse assays. Our findings highlight major implications for a better understanding of the role of a molecular modification on protein aggregation.

Characterization of Amyloidogenic Peptide Aggregability in Helical Subspace

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.

Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach

BACKGROUND

Graph theory and machine learning have been shown to be effective ways of classifying different stages of Alzheimer's disease (AD). Most previous studies have only focused on inter-subject classification with single-mode neuroimaging data. However, whether this classification can truly reflect the changes in the structure and function of the brain region in disease progression remains unverified. In the current study, we aimed to evaluate the classification framework, which combines structural Magnetic Resonance Imaging (sMRI) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) metrics, to distinguish mild cognitive impairment non-converters (MCInc)/AD from MCI converters (MCIc) by using graph theory and machine learning.

METHODS

With the intra-subject (MCInc vs. MCIc) and inter-subject (MCIc vs. AD) design, we employed cortical thickness features, structural brain network features, and sub-frequency (full-band, slow-4, slow-5) functional brain network features for classification. Three feature selection methods [random subset feature selection algorithm (RSFS), minimal redundancy maximal relevance (mRMR), and sparse linear regression feature selection algorithm based on stationary selection (SS-LR)] were used respectively to select discriminative features in the iterative combinations of MRI and network measures. Then support vector machine (SVM) classifier with nested cross-validation was employed for classification. We also compared the performance of multiple classifiers (Random Forest, K-nearest neighbor, Adaboost, SVM) and verified the reliability of our results by upsampling.

RESULTS

We found that in the classifications of MCIc vs. MCInc, and MCIc vs. AD, the proposed RSFS algorithm achieved the best accuracies (84.71, 89.80%) than the other algorithms. And the high-sensitivity brain regions found with the two classification groups were inconsistent. Specifically, in MCIc vs. MCInc, the high-sensitivity brain regions associated with both structural and functional features included frontal, temporal, caudate, entorhinal, parahippocampal, and calcarine fissure and surrounding cortex. While in MCIc vs. AD, the high-sensitivity brain regions associated only with functional features included frontal, temporal, thalamus, olfactory, and angular.

CONCLUSIONS

These results suggest that our proposed method could effectively predict the conversion of MCI to AD, and the inconsistency of specific brain regions provides a novel insight for clinical AD diagnosis.

Administration of AAV-Alpha Synuclein NAC Antibody Improves Locomotor Behavior in Rats Overexpressing Alpha Synuclein

Accumulation of α-Synuclein (αSyn) in nigral dopaminergic neurons is commonly seen in patients with Parkinson′s disease (PD). We recently reported that transduction of intracellular single-chain intrabody targeting the 53–87 amino acid residues of human αSyn by recombinant adeno associated viral vector (AAV-NAC32) downregulated αSyn protein in SH-SY5Y cells and rat brain. This study characterizes the behavioral phenotype and dopaminergic protection in animals receiving AAV-NAC32. Our results show that adult DAT-Cre rats selectively overexpress αSyn in nigra dopaminergic neurons after local administration of AAV-DIO-αSyn. These animals develop PD-like phenotype, including bradykinesia and loss of tyrosine hydroxylase (TH) immunoreactivity in substantia nigra pars compacta dorsal tier (SNcd). An injection of AAV-NAC32 to nigra produces a selective antibody against αSyn and normalizes the behavior. AAV-NAC32 significantly increases TH, while reduces αSyn immunoreactivity in SNcd. Altogether, our data suggest that an AAV-mediated gene transfer of NAC32 antibody effectively antagonizes αSyn-mediated dopaminergic degeneration in nigra, which may be a promising therapeutic candidate for synucleinopathy or PD.

Simultaneous measurement of 1HC/N-R2's for rapid acquisition of backbone and sidechain paramagnetic relaxation enhancements (PREs) in proteins

Paramagnetic relaxation enhancements (PREs) are routinely used to provide long-range distance restraints for the determination of protein structures, to resolve protein dynamics, ligand-protein binding sites, and lowly populated species, using Nuclear Magnetic Resonance Spectroscopy (NMR). Here, we propose a simultaneous ¹H-¹⁵ N, ¹H-¹³C SESAME based pulse scheme for the rapid acquisition of ¹H^C/N-R₂ relaxation rates for the determination of backbone and sidechain PREs of proteins. The ¹H^N-R₂ rates from the traditional and our approach on Ubiquitin (UBQ) are well correlated (R² = 0.99), revealing their potential to be used quantitatively. Comparison of the S57C UBQ calculated and experimental PREs provided backbone and side chain Q factors of 0.23 and 0.24, respectively, well-fitted to the UBQ NMR structure, showing that our approach can be used to acquire accurate PRE rates from the functionally important sites of proteins but in at least half the time as traditional methods.

The Role of α-Synuclein in Methamphetamine-Induced Neurotoxicity

Methamphetamine (METH), a highly addictive psychostimulant, is the second most widely used illicit drug. METH produces damage dopamine neurons and apoptosis via multiple inter-regulating mechanisms, including dopamine overload, hyperthermia, oxidative stress, mitochondria dysfunction, endoplasmic reticulum stress, protein degradation system dysfunction, and neuroinflammation. Increasing evidence suggests that chronic METH abuse is associated with neurodegenerative changes in the human brain and an increased risk of Parkinson's disease (PD). METH use and PD may share some common steps in causing neurotoxicity. Accumulation of α-synuclein, a presynaptic protein, is the pathological hallmark of PD. Intriguingly, α-synuclein upregulation and aggregation are also found in dopaminergic neurons in the substantia nigra in chronic METH users. This suggests α-synuclein may play a role in METH-induced neurotoxicity. The mechanism of α-synuclein cytotoxicity in PD has attracted considerable attention; however, how α-synuclein affects METH-induced neurotoxicity has not been reviewed. In this review, we summarize the relationship between METH use and PD, interdependent mechanisms that are involved in METH-induced neurotoxicity and the significance of α-synuclein upregulation in response to METH use. The identification of α-synuclein overexpression and aggregation as a contributor to METH-induced neurotoxicity may provide a novel therapeutic target for the treatment of the deleterious effect of this drug and drug addiction.

Cu2+, Ca2+, and methionine oxidation expose the hydrophobic α-synuclein NAC domain

α-Synuclein is an intrinsically disordered protein whose aggregation is related to Parkinson's disease and other neurodegenerative disorders. Metal cations are one of the main factors affecting the propensity of α-synuclein to aggregate, either by directly binding to it or by catalyzing the production of reactive oxygen species that oxidize it. His50, Asp121 and several additional C-terminal α-synuclein residues are binding sites for numerous metal cations, while methionine sulfoxidation occurs readily on this protein under oxidative stress conditions. Molecular dynamics simulations are an excellent tool to obtain a microscopic picture of how metal binding or methionine sulfoxidation alter the conformational preferences of α-synuclein and, hence, its aggregation propensity. In this work, we report the first coarse-grained molecular dynamics study comparing the conformational ensembles of the native protein, the protein bound to either Cu²⁺ or Ca²⁺ at its main binding sites, and the methionine-sulfoxidized protein. Our results suggest that these events alter the transient α-synuclein intramolecular contacts, inducing a greater solvent exposure of its hydrophobic, aggregation-prone NAC domain, in full agreement with a recent experimental study on Ca²⁺ binding. Moreover, metal-binding residues directly participate in the long-range contacts that shield this domain and regulate α-synuclein aggregation. These results provide a molecular-level rationalization of the enhanced fibrillation experimentally observed in the presence of Cu²⁺ or Ca²⁺ and the oligomerization induced by methionine sulfoxidation.

Unraveling the NaCl Concentration Effect on the First Stages of α-Synuclein Aggregation

Intraneuronal aggregation of the intrinsically disordered protein α-synuclein is at the core of Parkinson's disease and related neurodegenerative disorders. Several reports show that the concentration of salts in the medium heavily affects its aggregation rate and fibril morphology, but a characterization of the individual monomeric conformations underlying these effects is still lacking. In this work, we have applied our α-synuclein-optimized coarse-grained molecular dynamics approach to decipher the structural features of the protein monomer under a range of NaCl concentrations (0.0-1.0 M). The results show that key intramolecular contacts between the terminal domains are lost at intermediate concentrations (leading to extended conformations likely to fibrillate), but recovered at high concentrations (leading to compact conformations likely to evolve toward amorphous aggregates). The pattern of direct interactions of the terminal α-synuclein domains with Na⁺ and Cl^- ions plays a key role in explaining this effect. Our results are consistent with a recent study reporting a fibrillation enhancement at moderate NaCl concentrations but an inhibition at higher concentrations. The present work will contribute to improving our understanding of the structural features of monomeric α-synuclein, determining its NaCl-induced fibrillation propensity and the molecular basis of synucleinopathies, necessary for the future development of disease-halting therapies.

Alterations of Brain Networks in Alzheimer's Disease and Mild Cognitive Impairment: A Resting State fMRI Study Based on a Population-specific Brain Template

This study aimed to investigate the alterations in brain networks in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on a population-specific brain template. Previous studies on AD brain networks using graph theory rarely adopted brain templates specific for certain ethnicities. In this study, patients were divided into 3 groups: AD (n = 24), MCI (n = 27), and healthy controls (HCs, n = 33), and all of the subjects are Chinese. Functional brain networks were constructed for each group based on a Chinese brain template using resting-state functional magnetic resonance imaging (rs-fMRI) data; several graph metrics were calculated. Graph metrics with significant differences after false discovery rate (FDR) correction were analyzed with respect to correlations with four neuropsychological test scores: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Activities of Daily Living (ADL), and Clinical Dementia Rating (CDR), which assessed the subjects' cognitive functions and ability to engage in ADL. Graph metrics including assortativity coefficient, nodal degree centrality, nodal clustering coefficient, nodal efficiency, and nodal local efficiency of the frontal gyrus and cerebellum were significantly altered in AD and MCI compared with HC. Several graph metrics were significantly correlated with cognitive function and the ability to engage in daily activities. The findings suggest that altered graph metrics in the frontal gyrus may reflect brain plasticity, and that patients with MCI may have unique graph metric alterations in the cerebellum. Future graph analysis studies on functional brain networks in AD and MCI based on population-specific brain atlases for particular ethnicities may prove valuable.

Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet

Intrinsically-disordered protein (IDP) characterization was an amazing change of paradigm in our classical sequence-structure-function theory. Moreover, IDPs are over-represented in major disease pathways and are now often targeted using small molecules for therapeutic purposes. This has had created a complex continuum from order-that encompasses rigid and flexible regions-to disorder regions; the latter being not accessible through classical crystallographic methodologies. In X-ray structures, the notion of order is dictated by access to resolved atom positions, providing rigidity and flexibility information with low and high experimental B-factors, while disorder is associated with the missing (non-resolved) residues. Nonetheless, some rigid regions can be found in disorder regions. Using ensembles of IDPs, their local conformations were analyzed in the light of a structural alphabet. An entropy index derived from this structural alphabet allowed us to propose a continuum of states from rigidity to flexibility and finally disorder. In this study, the analysis was extended to comparing these results to disorder predictions, underlying a limited correlation, and so opening new ideas to characterize and predict disorder.

Role of Tyr-39 for the Structural Features of α-Synuclein and for the Interaction with a Strong Modulator of Its Amyloid Assembly

Recent studies suggest that Tyr-39 might play a critical role for both the normal function and the pathological dysfunction of α-synuclein (αS), an intrinsically disordered protein involved in Parkinson’s disease. We perform here a comparative analysis between the structural features of human αS and its Y39A, Y39F, and Y39L variants. By the combined application of site-directed mutagenesis, biophysical techniques, and enhanced sampling molecular simulations, we show that removing aromatic functionality at position 39 of monomeric αS leads to protein variants populating more compact conformations, conserving its disordered nature and secondary structure propensities. Contrasting with the subtle changes induced by mutations on the protein structure, removing aromaticity at position 39 impacts strongly on the interaction of αS with the potent amyloid inhibitor phthalocyanine tetrasulfonate (PcTS). Our findings further support the role of Tyr-39 in forming essential inter and intramolecular contacts that might have important repercussions for the function and the dysfunction of αS.

Distance-Based Metrics for Comparing Conformational Ensembles of Intrinsically Disordered Proteins

Intrinsically disordered proteins are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons because their conformational diversity precludes optimal superimposition of the atomic coordinates necessary for deriving common similarity measures such as the root mean-square deviation of these coordinates. Here, we introduce superimposition-free metrics that are based on computing matrices of the Cα-Cα distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the root mean-square difference between the medians of the Cα-Cα distance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of intrinsically disordered proteins derived using experimental restraints or by molecular simulations and for proteins containing both structured and disordered regions.

Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI

Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01-0.08 Hz; slow-4: 0.027-0.08 Hz; slow-5: 0.01-0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI-EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer's disease at an early stage.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Lipopolysaccharide from Gut Microbiota Modulates α-Synuclein Aggregation and Alters Its Biological Function

Altered intestinal permeability has been correlated with Parkinson's pathophysiology in the enteric nervous system, before manifestations in the central nervous system (CNS). The inflammatory endotoxin or lipopolysaccharide (LPS) released by gut bacteria is known to modulate α-synuclein amyloidogenesis through the formation of intermediate nucleating species. Here, biophysical techniques in conjunction with microscopic images revealed the molecular interaction between lipopolysaccharide and α-synuclein that induce rapid nucleation events. This heteromolecular interaction stabilizes the α-helical intermediates in the α-synuclein aggregation pathway. Multitude NMR studies probed the residues involved in the LPS-binding structural motif that modulates the nucleating forms, affecting the cellular internalization and associated cytotoxicity. Collectively, our data characterizes this heteromolecular interaction associated with an alternative pathway in Parkinson's disease progression.

Conformational ensemble of native α-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations

Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction. Here, we apply a recently-developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations (CL-DMD) for the determination of conformational ensemble of the intrinsically disordered protein α-synuclein in the solution. The predicted structures were in agreement with hydrogen-deuterium exchange, circular dichroism, surface modification, and long-distance crosslinking data. We found that α-synuclein is present in solution as an ensemble of rather compact globular conformations with distinct topology and inter-residue contacts, which is well-represented by movements of the large loops and formation of few transient secondary structure elements. Non-amyloid component and C-terminal regions were consistently found to contain β-structure elements and hairpins.

As the population ages, neurodegenerative diseases such as Parkinson’s disease will become an increasing problem in many countries. Aggregation of the protein α-synuclein is the primary cause of Parkinson’s disease, but there is still a dearth of structural information pertaining to the native, non-aggregating form of this protein. A better understanding the structural state of the native protein may prove useful for the design of new therapeutics to combat this disease. In order to obtain more structural information on this protein, we have recently modelled the native α-synuclein protein. These models were generated using a novel approach which combines protein crosslinking and discrete molecular dynamics simulations. We have found that the α-synuclein protein can adopt several shapes, all with a similar topology, resembling a three fingered closed claw. A region of the protein important for aggregation was found to be protected from the surrounding biological environment in these conformations, and the stabilization of these structures may be a fruitful avenue for future drug research into mitigating the cause and effect of Parkinson’s disease.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), ³J-couplings (³Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Interactions between the Intrinsically Disordered Proteins β-Synuclein and α-Synuclein

Several intrinsically disordered proteins have been implicated in the process of amyloid fibril formation in neurodegenerative disease, and developing approaches to inhibit the aggregation of these intrinsically disordered proteins is critical for establishing effective therapies against disease progression. The aggregation pathway of the intrinsically disordered protein alpha-synuclein, which is implicated in several neurodegenerative diseases known as synucleinopathies, has been extensively characterized. Less attention has been leveraged on beta-synuclein, a homologous intrinsically disordered protein that co-localizes with alpha-synuclein and is known to delay alpha-synuclein fibril formation. In this review, we focus on beta-synuclein and the molecular-level interactions between alpha-synuclein and beta-synuclein that underlie the delay of fibril formation. We highlight studies that begin to define alpha-synuclein and beta-synuclein interactions at the monomer, oligomer, and surface levels, and suggest that beta-synuclein plays a role in regulation of inhibition at many different stages of alpha-synuclein aggregation.

Formation of Heterotypic Amyloids: α-Synuclein in Co-Aggregation

Protein misfolding resulting in the formation of ordered amyloid aggregates is associated with a number of devastating human diseases. Intrinsically disordered proteins (IDPs) do not autonomously fold up into a unique stable conformation and remain as an ensemble of rapidly fluctuating conformers. Many IDPs are prone to convert into the β-rich amyloid state. One such amyloidogenic IDP is α-synuclein that is involved in Parkinson's disease. Recent studies have indicated that other neuronal proteins, especially IDPs, can co-aggregate with α-synuclein in many pathological ailments. This article describes several such observations highlighting the role of heterotypic protein-protein interactions in the formation of hetero-amyloids. It is believed that the characterizations of molecular cross talks between amyloidogenic proteins as well as the mechanistic studies of heterotypic protein aggregation will allow us to decipher the role of the interacting proteins in amyloid proteomics.

Inferring properties of disordered chains from FRET transfer efficiencies

Förster resonance energy transfer (FRET) is a powerful tool for elucidating both structural and dynamic properties of unfolded or disordered biomolecules, especially in single-molecule experiments. However, the key observables, namely, the mean transfer efficiency and fluorescence lifetimes of the donor and acceptor chromophores, are averaged over a broad distribution of donor-acceptor distances. The inferred average properties of the ensemble therefore depend on the form of the model distribution chosen to describe the distance, as has been widely recognized. In addition, while the distribution for one type of polymer model may be appropriate for a chain under a given set of physico-chemical conditions, it may not be suitable for the same chain in a different environment so that even an apparently consistent application of the same model over all conditions may distort the apparent changes in chain dimensions with variation of temperature or solution composition. Here, we present an alternative and straightforward approach to determining ensemble properties from FRET data, in which the polymer scaling exponent is allowed to vary with solution conditions. In its simplest form, it requires either the mean FRET efficiency or fluorescence lifetime information. In order to test the accuracy of the method, we have utilized both synthetic FRET data from implicit and explicit solvent simulations for 30 different protein sequences, and experimental single-molecule FRET data for an intrinsically disordered and a denatured protein. In all cases, we find that the inferred radii of gyration are within 10% of the true values, thus providing higher accuracy than simpler polymer models. In addition, the scaling exponents obtained by our procedure are in good agreement with those determined directly from the molecular ensemble. Our approach can in principle be generalized to treating other ensemble-averaged functions of intramolecular distances from experimental data.

An Extended Guinier Analysis for Intrinsically Disordered Proteins

Guinier analysis allows model-free determination of the radius of gyration (R_g) of a biomolecule from X-ray or neutron scattering data, in the limit of very small scattering angles. Its range of validity is well understood for globular proteins, but is known to be more restricted for unfolded or intrinsically disordered proteins (IDPs). We have used ensembles of disordered structures from molecular dynamics simulations to investigate which structural properties cause deviations from the Guinier approximation at small scattering angles. We find that the deviation from the Guinier approximation is correlated with the polymer scaling exponent ν describing the unfolded ensemble. We therefore introduce an empirical, ν-dependent, higher-order correction term, to augment the standard Guinier analysis. We test the new fitting scheme using all-atom simulation data for several IDPs and experimental data for both an IDP and a destabilized mutant of a folded protein. In all cases tested, we achieve an accuracy of the inferred R_g within ∼3% of the true R_g. The method is straightforward to implement and extends the range of validity to a maximum qR_g of ∼2 versus ∼1.1 for Guinier analysis. Compared with the Guinier or Debye approaches, our method allows data from wider angles with lower noise to be used to analyze scattering data accurately. In addition to R_g, our fitting scheme also yields estimates of the scaling exponent ν in excellent agreement with the reference ν determined from the underlying molecular ensemble.

Role of Sporadic Parkinson Disease Associated Mutations A18T and A29S in Enhanced α-Synuclein Fibrillation and Cytotoxicity

Deposition of presynaptic protein α-synuclein in Lewy bodies and Lewy neurites in the substantia nigra region of brain has been linked with the clinical symptoms of the Parkinson's disease (PD). Proteotoxic stress conditions and mutations that cause abnormal aggregation of α-synuclein have close association with onset of PD and its progression. Therefore, studies pertaining to α-synuclein mutations play important roles in mechanistic understanding of aggregation behavior of the protein and subsequent pathology. Herein, guided by this fact, we have studied the aggregation kinetics, morphology, and neurotoxic effects of the two newly discovered sporadic PD associated mutants A18T and A29S of α-synuclein. Our studies demonstrate that both of the mutants are aggregation prone and undergo rapid aggregation compared to wild-type α-synuclein. Further, it was found that A18T mutant followed faster aggregation kinetics compared to A29S substitution. Additionally, we have designed three point mutations of α-synuclein for better understanding of the effects of substitutions on protein aggregation and demonstrated that substitution of alanine at the 18th position is highly sensitive compared to adjacent positions. Our results provide better understanding of the effects of α-synuclein mutations on its aggregation behavior that may be important in development of PD pathology.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.

A pH-dependent switch promotes β-synuclein fibril formation via glutamate residues

α-Synuclein (αS) is the primary protein associated with Parkinson's disease, and it undergoes aggregation from its intrinsically disordered monomeric form to a cross-β fibrillar form. The closely related homolog β-synuclein (βS) is essentially fibril-resistant under cytoplasmic physiological conditions. Toxic gain-of-function by βS has been linked to dysfunction, but the aggregation behavior of βS under altered pH is not well-understood. In this work, we compare fibril formation of αS and βS at pH 7.3 and mildly acidic pH 5.8, and we demonstrate that pH serves as an on/off switch for βS fibrillation. Using αS/βS domain-swapped chimera constructs and single residue substitutions in βS, we localized the switch to acidic residues in the N-terminal and non-amyloid component domains of βS. Computational models of βS fibril structures indicate that key glutamate residues (Glu-31 and Glu-61) in these domains may be sites of pH-sensitive interactions, and variants E31A and E61A show dramatically altered pH sensitivity for fibril formation supporting the importance of these charged side chains in fibril formation of βS. Our results demonstrate that relatively small changes in pH, which occur frequently in the cytoplasm and in secretory pathways, may induce the formation of βS fibrils and suggest a complex role for βS in synuclein cellular homeostasis and Parkinson's disease.

Novel conformation-selective alpha-synuclein antibodies raised against different in vitro fibril forms show distinct patterns of Lewy pathology in Parkinson's disease

AIMS

The aim of this study was to test the hypothesis that different conformations of misfolded α-synuclein (α-syn) are present in Parkinson's disease (PD) brain.

METHODS

Using two previously characterized conformations of α-syn fibrils, we generated new conformation-selective α-syn monoclonal antibodies (mAbs). We then interrogated multiple brain regions in a well-characterized autopsy cohort of PD patients (n = 49) with these mAbs, Syn7015 and Syn9029.

RESULTS

Syn7015 detects Lewy bodies (LBs) and Lewy neurites (LNs) formed by pathological α-syn in all brain regions tested, and is particularly sensitive to LNs and small Lewy dots, inclusions believed to form early in the disease. Further, we observed colocalization between Syn7015 and an early marker of α-syn pathology formation, phospho-Ser129-α-syn, and a lack of extensive colocalization with markers of more mature pathology. In comparison, Syn9029 detects Lewy pathology in all regions examined, but indicates significantly fewer LNs than Syn7015. In addition, colocalization of Syn9029 with later markers of α-syn pathology maturation (ubiquitin and P62) suggests that the pathology detected by Syn9029 is older. Semiquantitative scoring of both LN and LB pathology in nine brain regions further established this trend, with Syn7015 LN scores consistently higher than Syn9029 LN scores.

CONCLUSIONS

Our data indicate that different conformations of α-syn pathology are present in PD brain and correspond to different stages of maturity for Lewy pathology. Regional analysis of Syn7015 and Syn9029 immunostaining also provides support for the Braak hypothesis that α-syn pathology advances through the brain.

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM

BACKGROUND

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI).

NEW METHOD

Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features.

RESULTS

Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD.

COMPARISON WITH EXISTING METHOD(S)

To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC.

CONCLUSION

Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion.

Conformational ensemble of human α-synuclein physiological form predicted by molecular simulations

We perform here enhanced sampling simulations of N-terminally acetylated human α-synuclein, an intrinsically disordered protein involved in Parkinson's disease. The calculations, consistent with experiments, suggest that the post-translational modification leads to the formation of a transient amphipathic α-helix. The latter, absent in the non-physiological form, alters protein dynamics at the N-terminal and intramolecular interactions.

Consistent View of Polypeptide Chain Expansion in Chemical Denaturants from Multiple Experimental Methods

There has been a long-standing controversy regarding the effect of chemical denaturants on the dimensions of unfolded and intrinsically disordered proteins: A wide range of experimental techniques suggest that polypeptide chains expand with increasing denaturant concentration, but several studies using small-angle X-ray scattering (SAXS) have reported no such increase of the radius of gyration (Rg). This inconsistency challenges our current understanding of the mechanism of chemical denaturants, which are widely employed to investigate protein folding and stability. Here, we use a combination of single-molecule Förster resonance energy transfer (FRET), SAXS, dynamic light scattering (DLS), and two-focus fluorescence correlation spectroscopy (2f-FCS) to characterize the denaturant dependence of the unfolded state of the spectrin domain R17 and the intrinsically disordered protein ACTR in two different denaturants. Standard analysis of the primary data clearly indicates an expansion of the unfolded state with increasing denaturant concentration irrespective of the protein, denaturant, or experimental method used. This is the first case in which SAXS and FRET have yielded even qualitatively consistent results regarding expansion in denaturant when applied to the same proteins. To more directly illustrate this self-consistency, we used both SAXS and FRET data in a Bayesian procedure to refine structural ensembles representative of the observed unfolded state. This analysis demonstrates that both of these experimental probes are compatible with a common ensemble of protein configurations for each denaturant concentration. Furthermore, the resulting ensembles reproduce the trend of increasing hydrodynamic radius with denaturant concentration obtained by 2f-FCS and DLS. We were thus able to reconcile the results from all four experimental techniques quantitatively, to obtain a comprehensive structural picture of denaturant-induced unfolded state expansion, and to identify the most likely sources of earlier discrepancies.

Heat shock proteins as potential targets for protective strategies in neurodegeneration

Protein aggregates are hallmarks of nearly all age-related neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and several polyglutamine diseases such as Huntington's disease and different forms of spinocerebellar ataxias (SCA; SCA1-3, SCA6, and SCA7). The collapse of cellular protein homoeostasis can be both a cause and a consequence of this protein aggregation. Boosting components of the cellular protein quality control system has been widely investigated as a strategy to counteract protein aggregates or their toxic consequences. Heat shock proteins (HSPs) play a central part in regulating protein quality control and contribute to protein aggregation and disaggregation. Therefore, HSPs are viable targets for the development of drugs aimed at reducing pathogenic protein aggregates that are thought to contribute to the development of so many neurodegenerative disorders.

Drugs That Bind to α-Synuclein: Neuroprotective or Neurotoxic?

The misfolding of α-synuclein is a critical event in the death of dopaminergic neurons and the progression of Parkinson's disease. Drugs that bind to α-synuclein and form a loop structure between the N- and C-terminus tend to be neuroprotective, whereas others that cause a more compact structure tend to be neurotoxic. The binding of several natural products and other drugs that are involved in dopamine metabolism were investigated by nanopore analysis and isothermal titration calorimetry. The antinausea drugs, cinnarizine and metoclopramide, do not bind to α-synuclein, whereas amphetamine and the herbicides, paraquat and rotenone, bind tightly and cause α-synuclein to adopt a more compact conformation. The recreational drug, cocaine, binds to α-synuclein, whereas heroin and methadone do not. Metformin, which is prescribed for diabetes and is neuroprotective, binds well without causing α-synuclein to adopt a more compact conformation. Methylphenidate (ritalin) binds to sites in both the N- and C-terminus and causes α-synuclein to adopt a loop conformation. In contrast, amphetamine only binds to the N-terminus. Except for cinnarizine and metoclopramide, there is a good correlation between the mode of binding to α-synuclein and whether a drug is neuroprotective or neurotoxic.

Investigating protein conformational energy landscapes and atomic resolution dynamics from NMR dipolar couplings: a review

Nuclear magnetic resonance spectroscopy is exquisitely sensitive to protein dynamics. In particular inter-nuclear dipolar couplings, that become measurable in solution when the protein is dissolved in a dilute liquid crystalline solution, report on all conformations sampled up to millisecond timescales. As such they provide the opportunity to describe the Boltzmann distribution present in solution at atomic resolution, and thereby to map the conformational energy landscape in unprecedented detail. The development of analytical methods and approaches based on numerical simulation and their application to numerous biologically important systems is presented.

Computational approaches for inferring the functions of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are ubiquitously involved in cellular processes and often implicated in human pathological conditions. The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm. Unfortunately, investigating the characteristics and describing the structural behavior of IDPs is far from trivial, and inferring the function(s) of a disordered protein region remains a major challenge. Computational methods have proven particularly relevant for studying IDPs: on the sequence level their dependence on distinct characteristics determined by the local amino acid context makes sequence-based prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe the behavior of a flexible protein chain. Here, we offer an overview of the latest developments and computational techniques that aim to uncover how protein function is connected to intrinsic disorder.

α-Synuclein dimer structures found from computational simulations

Dimer formation is likely the first step in the oligomerization of α-synuclein in Lewy bodies. In order to prevent α-synuclein aggregation, knowledge of the atomistic structures of possible α-synuclein dimers and the interaction affinity between the dimer domains is a necessary prerequisite in the process of rational design of dimerization inhibitors. Using computational methodology, we have investigated several possible α-synuclein dimer structures, focusing on dimers formed from α-helical forms of the protein found when it is membrane-bound, and dimers formed from β-sheet conformations predicted by simulations. Structures and corresponding binding affinities for the interacting monomers in possible α-synuclein dimers, along with properties including the contributions from different interaction energies and the radii of gyration, were found through molecular docking followed by MD simulations and binding-energy calculations. We found that even though α-synuclein is highly charged, hydrophobic contributions play a significant role in stabilizing dimers.