Exploring the aggregation free energy landscape of the amyloid-β protein (1-40)

Sequence-based identification of amyloidogenic β-hairpins reveals a prostatic acid phosphatase fragment promoting semen amyloid formation

β-Structure-rich amyloid fibrils are hallmarks of several diseases, including Alzheimer's (AD), Parkinson's (PD), and type 2 diabetes (T2D). While amyloid fibrils typically consist of parallel β-sheets, the anti-parallel β-hairpin is a structural motif accessible to amyloidogenic proteins in their monomeric and oligomeric states. Here, to investigate implications of β-hairpins in amyloid formation, potential β-hairpin-forming amyloidogenic segments in the human proteome were predicted based on sequence similarity with β-hairpins previously observed in Aβ, α-synuclein, and islet amyloid polypeptide, amyloidogenic proteins associated with AD, PD, and T2D, respectively. These three β-hairpins, established upon binding to the engineered binding protein β-wrapin AS10, are characterized by proximity of two sequence segments rich in hydrophobic and aromatic amino acids, with high β-aggregation scores according to the TANGO algorithm. Using these criteria, 2505 potential β-hairpin-forming amyloidogenic segments in 2098 human proteins were identified. Characterization of a test set of eight protein segments showed that seven assembled into Thioflavin T-positive aggregates and four formed β-hairpins in complex with AS10 according to NMR. One of those is a segment of prostatic acid phosphatase (PAP) comprising amino acids 185-208. PAP is naturally cleaved into fragments, including PAP(248-286) which forms functional amyloid in semen. We find that PAP(185-208) strongly decreases the protein concentrations required for fibril formation of PAP(248-286) and of another semen amyloid peptide, SEM1(86-107), indicating that it promotes nucleation of semen amyloids. In conclusion, β-hairpin-forming amyloidogenic protein segments could be identified in the human proteome with potential roles in functional or disease-related amyloid formation.

Pathogenic Mutation ΔK280 Promotes Hydrophobic Interactions Involving Microtubule-Binding Domain and Enhances Liquid-Liquid Phase Separation of Tau

Liquid-liquid phase separation (LLPS) of tau protein can initiate its aggregation which is associated with Alzheimer's disease. The pathogenic mutation ΔK280 can enhance the aggregation of K18, a truncated tau variant comprising the microtubule-binding domain. However, the impact of ΔK280 on K18 LLPS and underlying mechanisms are largely unexplored. Herein, the conformational ensemble and LLPS of ΔK280 K18 through multiscale molecular simulations and microscopy experiments are investigated. All-atom molecular dynamic simulations reveal that ΔK280 significantly enhances the collapse degree and β-sheet content of the K18 monomer, indicating that ΔK280 mutation may promote K18 LLPS, validated by coarse-grained phase-coexistence simulations and microscopy experiments. Importantly, ΔK280 mutation promotes β-sheet formation of six motifs (especially PHF6), increases the hydrophobic solvent exposure of PHF6* and PHF6, and enhances hydrophobic, hydrogen bonding, and cation-π interactions involving most of the motifs, thus facilitating the phase separation of K18. Notably, ΔK280 alters the interaction network among the six motifs, inducing the formation of K18 conformations with high β-sheet contents and collapse degree. Coarse-grained simulations on full-length tau reveal that ΔK280 promotes tau LLPS by enhancing the hydrophobic interactions involving the microtubule-binding domain. These findings offer detailed mechanistic insights into ΔK280-induced tau pathogenesis, providing potential targets for therapeutic intervention.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Production of Distinct Fibrillar, Oligomeric, and Other Aggregation States from Network Models of Multibody Interaction

Protein aggregation can produce a wide range of states, ranging from fibrillar structures and oligomers to unstructured and semistructured gel phases. Recent work has shown that many of these states can be recapitulated by relatively simple, topological models specified in terms of multibody interaction energies, providing a direct connection between aggregate intermolecular forces and aggregation products. Here, we examine a low-dimensional network Hamiltonian model (NHM) based on four basic multibody interactions found in any aggregate system. We characterize the phase behavior of this NHM family, showing that fibrils arise from a balance between elongation-inducing and contact-inhibiting forces. Complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer's disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions. We characterize the phase behavior of this NHM family, demonstrating the range of ordered and disordered aggregation states possible with this set of interactions. As we show, fibrils arise from a balance between elongation-inducing and contact-inhibiting forces, existing in a regime bounded by gel-like and disaggregated phases; complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer's disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, allowing generalization to macroscopic systems, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions.

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin's Regulation on Actin Filaments

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experiments suggest that cofilin forms functionally distinct oligomers via thiol post-translational modifications (PTMs) that promote actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Utilizing a biasing scheme in simulations, we focus on analyzing dimer conformations conducive to disulfide bond formation. Additionally, we explore potential PTMs arising from the examined conformational ensemble. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. We also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol PTM of cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

A turn for the worse: Aβ β-hairpins in Alzheimer's disease

Amyloid-β (Aβ) oligomers are a cause of neurodegeneration in Alzheimer's disease (AD). These soluble aggregates of the Aβ peptide have proven difficult to study due to their inherent metastability and heterogeneity. Strategies to isolate and stabilize homogenous Aβ oligomer populations have emerged such as mutations, covalent cross-linking, and protein fusions. These strategies along with molecular dynamics simulations have provided a variety of proposed structures of Aβ oligomers, many of which consist of molecules of Aβ in β-hairpin conformations. β-Hairpins are intramolecular antiparallel β-sheets composed of two β-strands connected by a loop or turn. Three decades of research suggests that Aβ peptides form several different β-hairpin conformations, some of which are building blocks of toxic Aβ oligomers. The insights from these studies are currently being used to design anti-Aβ antibodies and vaccines to treat AD. Research suggests that antibody therapies designed to target oligomeric Aβ may be more successful at treating AD than antibodies designed to target linear epitopes of Aβ or fibrillar Aβ. Aβ β-hairpins are good epitopes to use in antibody development to selectively target oligomeric Aβ. This review summarizes the research on β-hairpins in Aβ peptides and discusses the relevance of this conformation in AD pathogenesis and drug development.

Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation

The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported in minutes or hours. This time scale deficit of roughly 9 orders of magnitude presents a major challenge in the design of computer simulation methods for studying protein aggregation events. Coarse-grained molecular simulations offer a computationally tractable path forward for exploring the molecular mechanism driving the formation of these structures, which are implicated in diseases such as Alzheimer's, Parkinson's, and type-II diabetes. Network Hamiltonian models of aggregation are centered around a Hamiltonian function that returns the total energy of a system of aggregating proteins, given the graph structure of the system as an input. In the graph, or network, representation of the system, each protein molecule is represented as a node, and noncovalent bonds between proteins are represented as edges. The parameter, i.e., a set of coefficients that determine the degree to which each topological degree of freedom is favored or disfavored, must be determined for each network Hamiltonian model, and is a well-known technical challenge. The methodology is first demonstrated by beginning with an initial set of randomly parametrized models of low fibril fraction (<5% fibrillar), and evolving to subsequent generations of models, ultimately leading to high fibril fraction models (>70% fibrillar). The methodology is also demonstrated by applying it to optimizing previously published network Hamiltonian models for the 5 key amyloid fibril topologies that have been reported in the Protein Data Bank (PDB). The models generated by the AI produced fibril fractions that surpass previously published fibril fractions in 3 of 5 cases, including the most naturally abundant amyloid fibril topology, the 1,2 2-ribbon, which features a steric zipper. The authors also aim to encourage more widespread use of the network Hamiltonian methodology for fitting a wide variety of self-assembling systems by releasing a free open-source implementation of the genetic algorithm introduced here.

The elementary reactions for incorporation into crystals

The growth rates of crystals are largely dictated by the chemical reaction between solute and kinks, in which a solute molecule severs its bonds with the solvent and establishes new bonds with the kink. Details on this sequence of bond breaking and rebuilding remain poorly understood. To elucidate the reaction at the kinks we employ four solvents with distinct functionalities as reporters on the microscopic structures and their dynamics along the pathway into a kink. We combine time-resolved in situ atomic force microscopy and x-ray and optical methods with molecular dynamics simulations. We demonstrate that in all four solvents the solute, etioporphyrin I, molecules reach the steps directly from the solution; this finding identifies the measured rate constant for step growth as the rate constant of the reaction between a solute molecule and a kink. We show that the binding of a solute molecule to a kink divides into two elementary reactions. First, the incoming solute molecule sheds a fraction of its solvent shell and attaches to molecules from the kink by bonds distinct from those in its fully incorporated state. In the second step, the solute breaks these initial bonds and relocates to the kink. The strength of the preliminary bonds with the kink determines the free energy barrier for incorporation into a kink. The presence of an intermediate state, whose stability is controlled by solvents and additives, may illuminate how minor solution components guide the construction of elaborate crystal architectures in nature and the search for solution compositions that suppress undesirable or accelerate favored crystallization in industry.

Acetone-induced structural variant of insulin amyloid fibrils

Self-propagating polymorphism of amyloid fibrils is a distinct manifestation of non-equilibrium conditions under which protein aggregation typically occurs. Structural variants of fibrils can often be accessed through physicochemical perturbations of the de novo aggregation process. On the other hand, tiny changes in the amino acid sequence of the parent protein may also result in structurally distinguishable amyloid fibrils. Here, we show that in the presence of acetone, the low-pH fibrillization pathway of bovine insulin (BI) leads to a new type of amyloid with the infrared features (split amide I' band with the maximum at 1623 cm-1) bearing a striking resemblance to those of the previously reported fibrils from recombinant LysB31-ArgB32 human insulin analog formed in the absence of the co-solvent. Insulin fibrils formed in the presence ([BI-ace]) and absence ([BI]) of acetone cross-seed each other and pass their infrared features to the daughter generations of fibrils. We have used dimethyl sulfoxide (DMSO) coupled to in situ infrared spectroscopy measurements to probe the stability of fibrils against chemical denaturation. While both types of fibrils eventually undergo DMSO-induced disassembly coupled to a β-sheet→coil transition, in the case of [BI-ace] amyloid, the denaturation is preceded by the fibrils transiently acquiring the [BI]-like infrared characteristics. We argue that this effect is caused by DMSO-induced dehydration of [BI-ace]. In support to this hypothesis, we show that, even in the absence of DMSO, the infrared features of [BI-ace] disappear upon drying. We discuss this very peculiar aspect of [BI-ace] fibrils in the context of recently accessed in silico models of plausible structural variants of insulin protofilaments.

Quantifying the energy landscape in weakly and strongly disordered frictional media

We investigate the "roughness" of the energy landscape of a system that diffuses in a heterogeneous medium with a random position-dependent friction coefficient α(x). This random friction acting on the system stems from spatial inhomogeneity in the surrounding medium and is modeled using the generalized Caldira-Leggett model. For a weakly disordered medium exhibiting a Gaussian random diffusivity D(x) = kBT/α(x) characterized by its average value ⟨D(x)⟩ and a pair-correlation function ⟨D(x1)D(x2)⟩, we find that the renormalized intrinsic diffusion coefficient is lower than the average one due to the fluctuations in diffusivity. The induced weak internal friction leads to increased roughness in the energy landscape. When applying this idea to diffusive motion in liquid water, the dissociation energy for a hydrogen bond gradually approaches experimental findings as fluctuation parameters increase. Conversely, for a strongly disordered medium (i.e., ultrafast-folding proteins), the energy landscape ranges from a few to a few kcal/mol, depending on the strength of the disorder. By fitting protein folding dynamics to the escape process from a metastable potential, the decreased escape rate conceptualizes the role of strong internal friction. Studying the energy landscape in complex systems is helpful because it has implications for the dynamics of biological, soft, and active matter systems.

Perspective for Molecular Dynamics Simulation Studies of Amyloid-β Aggregates

The cause of Alzheimer's disease is related to aggregates such as oligomers and amyloid fibrils consisting of amyloid-β (Aβ) peptides. Molecular dynamics (MD) simulation studies have been conducted to understand the molecular mechanism of the formation and disruption of Aβ aggregates. In this Perspective, the MD simulation studies are classified into four categories, focusing on the target systems: aggregation of Aβ peptides in bulk solution, Aβ aggregation at the interface, aggregation inhibitor against Aβ peptides, and nonequilibrium MD simulation of Aβ aggregates. MD simulation studies in these categories are first reviewed. Future perspectives in each category are then presented. Finally, the overall perspective is presented on how MD simulations of Aβ aggregates can be utilized for developing Alzheimer's disease treatment.

Nucleation of α-Synuclein Amyloid Fibrils Induced by Cross-Interaction with β-Hairpin Peptides Derived from Immunoglobulin Light Chains

Heterologous interactions between different amyloid-forming proteins, also called cross-interactions, may have a critical impact on disease-related amyloid formation. β-hairpin conformers of amyloid-forming proteins have been shown to affect homologous interactions in the amyloid self-assembly process. Here, we applied two β-hairpin-forming peptides derived from immunoglobulin light chains as models to test how heterologous β-hairpins modulate the fibril formation of Parkinson's disease-associated protein α-synuclein (αSyn). The peptides SMAhp and LENhp comprise β-strands C and C' of the κ4 antibodies SMA and LEN, which are associated with light chain amyloidosis and multiple myeloma, respectively. SMAhp and LENhp bind with high affinity to the β-hairpin-binding protein β-wrapin AS10 according to isothermal titration calorimetry and NMR spectroscopy. The addition of SMAhp and LENhp affects the kinetics of αSyn aggregation monitored by Thioflavin T (ThT) fluorescence, with the effect depending on assay conditions, salt concentration, and the applied β-hairpin peptide. In the absence of agitation, substoichiometric concentrations of the hairpin peptides strongly reduce the lag time of αSyn aggregation, suggesting that they support the nucleation of αSyn amyloid fibrils. The effect is also observed for the aggregation of αSyn fragments lacking the N-terminus or the C-terminus, indicating that the promotion of nucleation involves the interaction of hairpin peptides with the hydrophobic non-amyloid-β component (NAC) region.

Neural potentials of proteins extrapolate beyond training data

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Coarse-grained models allow computational investigation of biomolecular processes occurring on long time and length scales, intractable with atomistic simulation. Traditionally, many coarse-grained models rely mostly on pairwise interaction potentials. However, the decimation of degrees of freedom should, in principle, lead to a complex many-body effective interaction potential. In this work, we use experimental data on mutant stability to parametrize coarse-grained models for two proteins with and without many-body terms. We demonstrate that many-body terms are necessary to reproduce quantitatively the effects of point mutations on protein stability, particularly to implicitly take into account the effect of the solvent.

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Kinetic control in amyloid polymorphism: Different agitation and solution conditions promote distinct amyloid polymorphs of alpha-synuclein

Aggregation of neuronal protein α-synuclein is implicated in synucleinopathies, including Parkinson's disease. Despite abundant in vitro studies, the mechanism of α-synuclein assembly process remains ambiguous. In this work, α-synuclein aggregation was induced by its constant mixing in two separate modes, either by agitation in a 96-well microplate reader (MP) or in microcentrifuge tubes using a shaker incubator (SI). Aggregation in both modes occurred through a sigmoidal growth pattern with a well-defined lag, growth, and saturation phase. The end-stage MP- and SI-derived aggregates displayed distinct differences in morphological, biochemical, and spectral signatures as discerned through AFM, proteinase-K digestion, FTIR, Raman, and CD spectroscopy. The MP-derived aggregates showed irregular morphology with a significant random coil conformation, contrary to SI-derived aggregates, which showed typical β-sheet fibrillar structures. The end-stage MP aggregates convert to β-rich SI-like aggregates upon 1) seeding with SI-derived aggregates and 2) agitating in SI. We conclude that end-stage MP aggregates were in a kinetically trapped conformation, whose kinetic barrier was bypassed upon either seeding by SI-derived fibrils or shaking in SI. We further show that MP-derived aggregates that form in the presence of sorbitol, an osmolyte, displayed a β-rich signature, indicating that the preferential exclusion effect of osmolytes helped overcome the kinetic barrier. Our findings help in unravelling the kinetic origin of different α-synuclein aggregated polymorphs (strains) that encode diverse variants of synucleinopathies. We demonstrate that kinetic control shapes the polymorphic landscape of α-synuclein aggregates, both through de novo generation of polymorphs, and by their interconversion.

Protein aggregation: Consequences, mechanism, characterization and inhibitory strategies

Proteins play a major role in the regulation of various cellular functions including the synthesis of structural components. But proteins are stable under physiological conditions only. A slight variation in environmental conditions can cost them huge in terms of conformational stability ultimately leading to aggregation. Under normal conditions, aggregated proteins are degraded or removed from the cell by a quality control system including ubiquitin-proteasomal machinery and autophagy. But they are burdened under diseased conditions or are impaired by the aggregated proteins leading to the generation of toxicity. The misfolding and aggregation of protein such as amyloid-β, α-synuclein, human lysozyme etc., are responsible for certain diseases including Alzheimer, Parkinson, and non- neuropathic systemic amyloidosis respectively. Extensive research has been done to find the therapeutics for such diseases but till now we have got only symptomatic treatment that will reduce the disease severity but will not target the initial formation of nucleus responsible for disease progression and propagation. Hence there is an urgent need to develop the drugs targeting the cause of the disease. For this, a wide knowledge related to misfolding and aggregation under the same heading is required as described in this review alongwith the strategies hypothesized and implemented till now. This will contribute a lot to the work of researchers in the field of neuroscience.

Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers

Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and simulations on Aβ42 monomer point to random coil conformations with either transient helical or β-strand content. Our current conformational description of small Aβ42 oligomers is funneled toward amorphous aggregates with some β-sheet content and rare high energy states with well-ordered assemblies of β-sheets. In this study, we emphasize another view based on metastable α-helix bundle oligomers spanning the C-terminal residues, which are predicted by the machine-learning AlphaFold2 method and supported indirectly by low-resolution experimental data on many amyloid polypeptides. This finding has consequences in developing novel chemical tools and to design potential therapies to reduce aggregation and toxicity.

Machine learned coarse-grained protein force-fields: Are we there yet?

The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges.

Experiment and Simulation Reveal Residue Details for How Target Binding Tunes Calmodulin's Calcium-Binding Properties

We aim to elucidate the molecular mechanism of the reciprocal relation of calmodulin's (CaM) target binding and its affinity for calcium ions (Ca²⁺), which is central to decoding CaM-dependent Ca²⁺ signaling in a cell. We employed stopped-flow experiments and coarse-grained molecular simulations that learn the coordination chemistry of Ca²⁺ in CaM from first-principle calculations. The associative memories as part of the coarse-grained force fields built on known protein structures further influence CaM's selection of its polymorphic target peptides in the simulations. We modeled the peptides from the Ca²⁺/CaM-binding domain of Ca²⁺/CaM-dependent kinase II (CaMKII), CaMKIIp (293-310) and selected distinctive mutations at the N-terminus. Our stopped-flow experiments have shown that the CaM's affinity for Ca²⁺ in the bound complex of Ca²⁺/CaM/CaMKIIp decreased significantly when Ca²⁺/CaM bound to the mutant peptide (296-AAA-298) compared to that bound to the wild-type peptide (296-RRK-298). The coarse-grained molecular simulations revealed that the 296-AAA-298 mutant peptide destabilized the structures of Ca²⁺-binding loops at the C-domain of CaM (c-CaM) due to both loss of electrostatic interactions and differences in polymorphic structures. We have leveraged a powerful coarse-grained approach to advance a residue-level understanding of the reciprocal relation in CaM, that could not be possibly achieved by other computational approaches.

Governing dynamics and preferential binding of the AXH domain influence the aggregation pathway of Ataxin-1

The present state of understanding the mechanism of Spinocerebellar Ataxia-1, a fatal neurodegenerative disease linked to the protein Ataxin-1 (ATXN1), is baffled by a set of self-contradictory, and hence, inconclusive observations. This fallacy poses a bottleneck to the effective designing of curable drugs as the field is currently missing the specific druggable site. To understand the fundamentals of pathogenesis, we tried to decipher the intricacies of the extremely complicated landscape by targeting the relevant species that supposedly dictate the structure-function paradigm. The atomic-level description and characterization of the dynamism of the systems reveal the existence of structural polymorphism in all the leading stakeholders of the overall system. The very existence of conformational heterogeneity in every species creates numerous possible combinations of favorable interactions because of the variability in segmental cross-talks and hence claims its role in the choice of routes between functional activity and dysfunctional disease-causing aggregation. Despite this emergent configurational diversity, there is a common mode of operative intermolecular forces that dictates the extent of stability of all the multimeric complexes due to the localized population of a specific type of residue. The present research proposes a dynamic switch mechanism between aggregability and functional activity, based on the logical interpretation of the estimated variables, which is practically dictated by the effective concentration of the interacting species involved in the cell.

Early-Stage Oligomerization of Prion-like Polypeptides Reveals the Molecular Mechanism of Amyloid-Disrupting Capacity by Proline Residues

Proline cis/trans isomerization governs protein local conformational changes via its local mechanical rigidity. The amyloid-disrupting capacity of proline is widely acknowledged; however, the molecular mechanism is still not clear. To understand how proline residues in polypeptide chains influence amyloid propensity, we study several truncated sequences of the TDP-43 C-terminal region (287-322) and their triple proline variants (308PPP310). We use coarse-grained molecular simulation to study the time evolution of the process of aggregation in the early stages in an effective high-concentration condition (∼25 mM). This ensures the long time scales for protein association at laboratory concentrations. We use several experimentally determined structure templates as initial structures of monomer conformations. We carry out oligomer size analysis and cluster analysis, along with several structural measures, to characterize the size distributions of oligomers and their morphological/structural properties. We show that average oligomer size is not a good indicator of amyloid propensity. Structural order and/or morphological properties are better alternatives. We show that proline variants can efficiently maintain the formation of large "ordered" oligomers of shorter truncated sequences, i.e., 307-322. This "order" maintenance is weakened when using longer truncated sequences (i.e., 287-322), leading to the formation of "disordered" oligomers. From an energy trade-off perspective, if the entropic effect is weak (short sequence length), the shape-complementarity of proline variants effectively guides the oligomerization process to form "ordered" oligomer intermediates. This leads to a distinct aggregation pathway that promotes amyloid formation (on-pathway). Strong entropic effects (long sequence length), however, would cause the formation of "disordered" oligomers. This in turn will suppress amyloid formation (off-pathway). The proline shape-complementary effects provide a guided morphological restraint to facilitate the pathways of amyloid formation. Our study supports the importance of structure-based kinetic heterogeneity of prion-like sequence fragments in driving different aggregation pathways. This work sheds light on the role of morphological and structural order of early-stage oligomeric species in regulating amyloid-disrupting capacity by prolines.

Modeling structural interconversion in Alzheimers' amyloid beta peptide with classical and intrinsically disordered protein force fields

A comprehensive understanding of the aggregation mechanism in amyloid beta 42 (Aβ42) peptide is imperative for developing therapeutic drugs to prevent or treat Alzheimer's disease. Because of the high flexibility and lack of native tertiary structures of Aβ42, molecular dynamics (MD) simulations may help elucidate the peptide's dynamics with atomic details and collectively improve ensembles not seen in experiments. We applied microsecond-timescale MD simulations to investigate the dynamics and conformational changes of Aβ42 by using a newly developed Amber force field (ff14IDPSFF). We compared the ff14IDPSFF and the regular ff14SB force field by examining the conformational changes of two distinct Aβ42 monomers in explicit solvent. Conformational ensembles obtained by simulations depend on the force field and initial structure, Aβ42^α-helix or Aβ42^β-strand. The ff14IDPSFF sampled a high ratio of disordered structures and diverse β-strand secondary structures; in contrast, ff14SB favored helicity during the Aβ42^α-helix simulations. The conformations obtained from Aβ42^β-strand simulations maintained a balanced content in the disordered and helical structures when simulated by ff14SB, but the conformers clearly favored disordered and β-sheet structures simulated by ff14IDPSFF. The results obtained with ff14IDPSFF qualitatively reproduced the NMR chemical shifts well. In-depth peptide and cluster analysis revealed some characteristic features that may be linked to early onset of the fibril-like structure. The C-terminal region (mainly M35-V40) featured in-registered anti-parallel β-strand (β-hairpin) conformations with tested systems. Our work should expand the knowledge of force field and structure dependency in MD simulations and reveals the underlying structural mechanism-function relationship in Aβ42 peptides. Communicated by Ramaswamy H. Sarma.

Self-Assembly of Amyloid-Beta (Aβ) Peptides from Solution to Near In Vivo Conditions

Understanding the atomistic resolution changes during the self-assembly of amyloid peptides or proteins is important to develop compounds or conditions to alter the aggregation pathways and suppress the toxicity and potentially aid in the development of drugs. However, the complexity of protein aggregation and the transient order/disorder of oligomers along the pathways to fibril are very challenging. In this Perspective, we discuss computational studies of amyloid polypeptides carried out under various conditions, including conditions closely mimicking in vivo and point out the challenges in obtaining physiologically relevant results, focusing mainly on the amyloid-beta Aβ peptides.

Suppression of amyloid-β fibril growth by drug-engineered polymorph transformation

Fibrillization of the protein amyloid β is assumed to trigger Alzheimer's pathology. Approaches that target amyloid plaques, however, have garnered limited clinical success, and their failures may relate to the scarce understanding of the impact of potential drugs on the intertwined stages of fibrillization. Here, we demonstrate that bexarotene, a T-cell lymphoma medication with known antiamyloid activity both in vitro and in vivo, suppresses amyloid fibrillization by promoting an alternative fibril structure. We employ time-resolved in situ atomic force microscopy to quantify the kinetics of growth of individual fibrils and supplement it with structure characterization by cryo-EM. We show that fibrils with structure engineered by the drug nucleate and grow substantially slower than "normal" fibrils; remarkably, growth remains stunted even in drug-free solutions. We find that the suppression of fibril growth by bexarotene is not because of the drug binding to the fibril tips or to the peptides in the solution. Kinetic analyses attribute the slow growth of drug-enforced fibril polymorph to the distinctive dynamics of peptide chain association to their tips. As an additional benefit, the bexarotene fibrils kill primary rat hippocampal neurons less efficiently than normal fibrils. In conclusion, the suggested drug-driven polymorph transformation presents a mode of action to irreversibly suppress toxic aggregates not only in Alzheimer's but also potentially in myriad diverse pathologies that originate with protein condensation.

Free Gangliosides Can Alter Amyloid-β Aggregation

A recently proposed lipid-chaperone hypothesis suggests that free lipid molecules, not bound to membranes, affect the aggregation of amyloidogenic peptides such as amyloid-β (Aβ) peptides, whose aggregates are the hallmarks of Alzheimer's disease. Here, we combine experiments with all-atom molecular dynamics simulations in explicit solvent to explore the effects of neuronal ganglioside GM1, abundant in mammalian brains, on the aggregation of two principal isoforms of Aβ, Aβ40 and Aβ42. Our simulations show that free GM1 forms stable, highly water-soluble complexes with both isoforms, and nuclear magnetic resonance experiments support the formation of well-ordered, structurally compact GM1+Aβ complexes. By simulation, we also show that Aβ40 monomers display a preference for binding to GM1-containing hetero-oligomers over GM1-lacking homo-oligomers, while Aβ42 monomers have the opposite preference. These observations explain why GM1 dose-dependently inhibits Aβ40 aggregation but has no effect on Aβ42 aggregation, as assessed by thioflavin T fluorescence.

Folding Mechanism and Aggregation Propensity of the KH0 Domain of FMRP and Its R138Q Pathological Variant

The K-homology (KH) domains are small, structurally conserved domains found in proteins of different origins characterized by a central conserved βααβ “core” and a GxxG motif in the loop between the two helices of the KH core. In the eukaryotic KHI type, additional αβ elements decorate the “core” at the C-terminus. Proteins containing KH domains perform different functions and several diseases have been associated with mutations in these domains, including those in the fragile X mental retardation protein (FMRP). FMRP is an RNA-binding protein crucial for the control of RNA metabolism whose lack or mutations lead to fragile X syndrome (FXS). Among missense mutations, the R138Q substitution is in the KH0 degenerated domain lacking the classical GxxG motif. By combining equilibrium and kinetic experiments, we present a characterization of the folding mechanism of the KH0 domain from the FMRP wild-type and of the R138Q variant showing that in both cases the folding mechanism implies the accumulation of an on-pathway transient intermediate. Moreover, by exploiting a battery of biophysical techniques, we show that the KH0 domain has the propensity to form amyloid-like aggregates in mild conditions in vitro and that the R138Q mutation leads to a general destabilization of the protein and to an increased fibrillogenesis propensity.

Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA

Bacteriophage T7 gp4 helicase has served as a model system for understanding mechanisms of hexameric replicative helicase translocation. The mechanistic basis of how nucleoside 5'-triphosphate hydrolysis and translocation of gp4 helicase are coupled is not fully resolved. Here, we used a thermodynamically benchmarked coarse-grained protein force field, Associative memory, Water mediated, Structure and Energy Model (AWSEM), with the single-stranded DNA (ssDNA) force field 3SPN.2C to investigate gp4 translocation. We found that the adenosine 5'-triphosphate (ATP) at the subunit interface stabilizes the subunit-subunit interaction and inhibits subunit translocation. Hydrolysis of ATP to adenosine 5'-diphosphate enables the translocation of one subunit, and new ATP binding at the new subunit interface finalizes the subunit translocation. The LoopD2 and the N-terminal primase domain provide transient protein-protein and protein-DNA interactions that facilitate the large-scale subunit movement. The simulations of gp4 helicase both validate our coarse-grained protein-ssDNA force field and elucidate the molecular basis of replicative helicase translocation.

Exploring the Interplay between Disordered and Ordered Oligomer Channels on the Aggregation Energy Landscapes of α-Synuclein

The abnormal aggregation of α-synulcein is associated with multiple neurodegenerative diseases such as Parkinson's disease. The hydrophobic non-amyloid component (NAC) region of α-synuclein comprises the core of the fibril in vitro and in vivo. In this work, we study the aggregation landscape of the hydrophobic NAC region of α-synuclein using a transferrable coarse-grained force field, the associative memory water-mediated structure, and energy model (AWSEM). Using structural similarity, we can group metastable states on the free energy landscape of aggregation into three types of oligomers: disordered oligomers, prefibrillar oligomers with disordered tips, and ordered prefibrillar oligomers. The prefibrillar oligomers with disordered tips have more in-register parallel β strands than do the fully disordered oligomers but have fewer in-register parallel β strands than the ordered prefibrillar oligomers. Along with the ordered prefibrillar species, the disordered oligomeric states dominate at small oligomer sizes while the prefibrillar species with disordered tips thermodynamically dominate with the growth of oligomers. The topology of the aggregation landscape and observations in simulations suggest there is backtracking between ordered prefibrillar oligomers and other kinds of oligomers as the aggregation proceeds. The significant structural differences between the ordered prefibrillar oligomers and the disordered oligomers support the idea that the growth of these two kinds of oligomers involves kinetically independent parallel pathways. In contrast, the overall structural similarity between the fully ordered prefibrillar oligomers and the prefibrillar oligomers with disordered tips implies that two channels can interconvert on slower time scales. We also evaluate the effects of phosphorylation on the aggregation free energy landscape using statistical mechanical perturbation theory.

Pathway Dependence of the Formation and Development of Prefibrillar Aggregates in Insulin B Chain

Amyloid fibrils have been an important subject as they are involved in the development of many amyloidoses and neurodegenerative diseases. The formation of amyloid fibrils is typically initiated by nucleation, whereas its exact mechanisms are largely unknown. With this situation, we have previously identified prefibrillar aggregates in the formation of insulin B chain amyloid fibrils, which have provided an insight into the mechanisms of protein assembly involved in nucleation. Here, we have investigated the formation of insulin B chain amyloid fibrils under different pH conditions to better understand amyloid nucleation mediated by prefibrillar aggregates. The B chain showed strong propensity to form amyloid fibrils over a wide pH range, and prefibrillar aggregates were formed under all examined conditions. In particular, different structures of amyloid fibrils were found at pH 5.2 and pH 8.7, making it possible to compare different pathways. Detailed investigations at pH 5.2 in comparison with those at pH 8.7 have suggested that the evolution of protofibril-like aggregates is a common mechanism. In addition, different processes of evolution of the prefibrillar aggregates have also been identified, suggesting that the nucleation processes diversify depending on the polymorphism of amyloid fibrils.

General Principles Underpinning Amyloid Structure

Amyloid fibrils are a pathologically and functionally relevant state of protein folding, which is generally accessible to polypeptide chains and differs fundamentally from the globular state in terms of molecular symmetry, long-range conformational order, and supramolecular scale. Although amyloid structures are challenging to study, recent developments in techniques such as cryo-EM, solid-state NMR, and AFM have led to an explosion of information about the molecular and supramolecular organization of these assemblies. With these rapid advances, it is now possible to assess the prevalence and significance of proposed general structural features in the context of a diverse body of high-resolution models, and develop a unified view of the principles that control amyloid formation and give rise to their unique properties. Here, we show that, despite system-specific differences, there is a remarkable degree of commonality in both the structural motifs that amyloids adopt and the underlying principles responsible for them. We argue that the inherent geometric differences between amyloids and globular proteins shift the balance of stabilizing forces, predisposing amyloids to distinct molecular interaction motifs with a particular tendency for massive, lattice-like networks of mutually supporting interactions. This general property unites previously characterized structural features such as steric and polar zippers, and contributes to the long-range molecular order that gives amyloids many of their unique properties. The shared features of amyloid structures support the existence of shared structure-activity principles that explain their self-assembly, function, and pathogenesis, and instill hope in efforts to develop broad-spectrum modifiers of amyloid function and pathology.

Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42

There are various diseases caused by protein aggregation such as Alzheimer’s, Parkinson’s, and Huntington’s diseases. From the diversity in the fibril structure, aggregation is expected to occur via heterogeneous pathways. However, characterization of this heterogeneity is extremely difficult because it requires following individual fibril formation in a mixture from early oligomerization stages. In this work, we investigated aggregation of the 42-residue isoform of amyloid β (Aβ42) using single-molecule fluorescence imaging and deep learning. We could track the growth of individual fibrils, which allows for a quantitative description of heterogeneous fibril formation and discovery of a new fibril nucleation mechanism. Further characterization of heterogeneity involving Aβ42 will be important for better understanding the disease mechanism.

Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is the key to understanding the aggregation mechanism and toxicity, but in practice it is extremely difficult to probe individual aggregation pathways in a mixture. Here, we present development of a method combining single-molecule fluorescence lifetime imaging and deep learning for monitoring individual fibril formation in real time and their high-throughput analysis. A deep neural network (FNet) separates an image of highly overlapping fibrils into single fibril images, which allows for tracking the growth and changes in characteristics of individual fibrils. Using this method, we investigated aggregation of the 42-residue amyloid-β peptide (Aβ42). We demonstrate that highly heterogeneous fibril formation can be quantitatively characterized in terms of the number of cross-β subunits, elongation speed, growth polarity, and conformation of fibrils. Tracking individual fibril formation and growth also leads to the discovery of a general nucleation mechanism (termed heterogeneous secondary nucleation), where a fibril is formed on the surface of an oligomer with a different structure. Our development will be broadly applicable to characterization of heterogeneous aggregation processes of other proteins.

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.

Examining the Ensembles of Amyloid-β Monomer Variants and Their Propensities to Form Fibers Using an Energy Landscape Visualization Method

The amyloid-β (Aβ) monomer, an intrinsically disordered peptide, is produced by the cleavage of the amyloid precursor protein, leading to Aβ-40 and Aβ-42 as major products. These two isoforms generate pathological aggregates, whose accumulation correlates with Alzheimer's disease (AD). Experiments have shown that even though the natural abundance of Aβ-42 is smaller than that for Aβ-40, the Aβ-42 is more aggregation-prone compared to Aβ-40. Moreover, several single-point mutations are associated with early onset forms of AD. This work analyzes coarse-grained associative-memory, water-mediated, structure and energy model (AWSEM) simulations of normal Aβ-40 and Aβ-42 monomers, along with six single-point mutations associated with early onset disease. We analyzed the simulations using the energy landscape visualization method (ELViM), a reaction-coordinate-free approach suited to explore the frustrated energy landscapes of intrinsically disordered proteins. ELViM is shown to distinguish the monomer ensembles of variants that rapidly form fibers from those that do not form fibers as readily. It also delineates the amino acid contacts characterizing each ensemble. The results shed light on the potential of ELViM to probe intrinsically disordered proteins.

Dynamical Manifestations of Supercooling in Amyloid Hydration

The effect of extreme temperature on amyloidogenic species remains sparsely explored. In a recent study (J. Phys. Chem. Lett., 2019, 10, (10)), we employed exhaustive molecular dynamics simulations to explore the cold thermal response of a putative small amyloid oligomer and to elicit the role of solvent modulation. Herein, we investigate the dynamical response of the hydration waters of the oligomer within the supercooled states. Using NMR-based formalism, we delineate the entropic response in terms of the side-chain conformational entropy that corroborates the weakening of the hydrophobic core with lowering of temperature. The translational dynamics of the protein and hydration waters reveal the coupling of protein dynamical fluctuations with solvent dynamics under supercooled conditions. Probing the translational motion as a space-time correlation indicates glassy dynamics exhibited by hydration waters in the supercooled regime. Caging of the water molecules with lowering of temperature and the resultant hopping dynamics are reflected in the longer β-relaxation timescales of translational motion. Furthermore, we utilized mode-coupling theory (MCT) and derived the ideal glass transition temperature from translational and rotational dynamics, around ∼196 and 209 K, respectively. Interestingly, rotational motion in the supercooled regime deviates from the MCT law, exhibits Arrhenius motion, and marks a fragile-to-strong crossover at 227 K. The low-frequency vibrational modes also coincide with the dynamical transition. This exposition lends dynamical insights into the hydration coupling of an amyloid aggregate under cryogenic conditions.

Peptide backbone modifications of amyloid β (1-40) impact fibrillation behavior and neuronal toxicity

Fibril formation of amyloid β (Aβ) peptides is one of the key molecular events connected to Alzheimer's disease. The pathway of formation and mechanism of action of Aβ aggregates in biological systems is still object of very active research. To this end, systematic modifications of the Phe19-Leu34 hydrophobic contact, which has been reported in almost all structural studies of Aβ40 fibrils, helps understanding Aβ folding pathways and the underlying free energy landscape of the amyloid formation process. In our approach, a series of Aβ40 peptide variants with two types of backbone modifications, namely incorporation of (i) a methylene or an ethylene spacer group and (ii) a N-methylation at the amide functional group, of the amino acids at positions 19 or 34 was applied. These mutations are expected to challenge the inter-β-strand side chain contacts as well as intermolecular backbone β-sheet hydrogen bridges. Using a multitude of biophysical methods, it is shown that these backbone modifications lead, in most of the cases, to alterations in the fibril formation kinetics, a higher local structural heterogeneity, and a somewhat modified fibril morphology without generally impairing the fibril formation capacity of the peptides. The toxicological profile found for the variants depend on the type and extent of the modification.

Systematic Search for a Predictor for the Clinical Observables of Alzheimer's Disease

One of the prevailing life-threatening incurable neurodegenerative diseases that are presently endangering human society as a whole, and hence, baffling the entire spectrum of the scientific and pharmaceutical world, is Alzheimer's disease (AD). AD is a manifestation of self-assembly of both wild-type (sporadic) and mutated (familial) forms of the amyloid-β peptide, a proteolytic product of the amyloid precursor protein, where the self-assembly results in the genesis of pathogenic fibrillar aggregates. Currently prevailing diagnostic and hence therapeutic challenges originate from the unavailability of a specific predictor for clinical observables. The continuous emergence of novel pathogenic mutants with unpredictable phenotypes adds immensely to the nonspecific nature of the problem. The current research reports a simple physical parameter, the binding affinity of a protofilament to its protofibril, which predicts the clinical observables of familial AD with astounding accuracy and more importantly, without any adjustable parameters.

Frustrated peptide chains at the fibril tip control the kinetics of growth of amyloid-β fibrils

Amyloid fibrillization is an exceedingly complex process in which incoming peptide chains bind to the fibril while concertedly folding. The coupling between folding and binding is not fully understood. We explore the molecular pathways of association of Aβ40 monomers to fibril tips by combining time-resolved in situ scanning probe microscopy with molecular modeling. The comparison between experimental and simulation results shows that a complex supported by nonnative contacts is present in the equilibrium structure of the fibril tip and impedes fibril growth in a supersaturated solution. The unraveling of this frustrated state determines the rate of fibril growth. The kinetics of growth of freshly cut fibrils, in which the bulk fibril structure persists at the tip, complemented by molecular simulations, indicate that this frustrated complex comprises three or four monomers in nonnative conformations and likely is contained on the top of a single stack of peptide chains in the fibril structure. This pathway of fibril growth strongly deviates from the common view that the conformational transformation of each captured peptide chain is templated by the previously arrived peptide. The insights into the ensemble structure of the frustrated complex may guide the search for suppressors of Aβ fibrillization. The uncovered dynamics of coupled structuring and assembly during fibril growth are more complex than during the folding of most globular proteins, as they involve the collective motions of several peptide chains that are not guided by a funneled energy landscape.

The N-terminal domain of RfaH plays an active role in protein fold-switching

The bacterial elongation factor RfaH promotes the expression of virulence factors by specifically binding to RNA polymerases (RNAP) paused at a DNA signal. This behavior is unlike that of its paralog NusG, the major representative of the protein family to which RfaH belongs. Both proteins have an N-terminal domain (NTD) bearing an RNAP binding site, yet NusG C-terminal domain (CTD) is folded as a β-barrel while RfaH CTD is forming an α-hairpin blocking such site. Upon recognition of the specific DNA exposed by RNAP, RfaH is activated via interdomain dissociation and complete CTD structural rearrangement into a β-barrel structurally identical to NusG CTD. Although RfaH transformation has been extensively characterized computationally, little attention has been given to the role of the NTD in the fold-switching process, as its structure remains unchanged. Here, we used Associative Water-mediated Structure and Energy Model (AWSEM) molecular dynamics to characterize the transformation of RfaH, spotlighting the sequence-dependent effects of NTD on CTD fold stabilization. Umbrella sampling simulations guided by native contacts recapitulate the thermodynamic equilibrium experimentally observed for RfaH and its isolated CTD. Temperature refolding simulations of full-length RfaH show a high success towards α-folded CTD, whereas the NTD interferes with βCTD folding, becoming trapped in a β-barrel intermediate. Meanwhile, NusG CTD refolding is unaffected by the presence of RfaH NTD, showing that these NTD-CTD interactions are encoded in RfaH sequence. Altogether, these results suggest that the NTD of RfaH favors the α-folded RfaH by specifically orienting the αCTD upon interdomain binding and by favoring β-barrel rupture into an intermediate from which fold-switching proceeds.

Proteins commonly adopt a single three-dimensional structure that is required for biological function. Nevertheless, proteins are not isolated in the cell, and the presence of binding partners can give rise to alternate structural configurations. Metamorphic proteins represent an extreme case of the latter, by folding into at least two well-defined configurations that are both structurally and functionally different. For RfaH, a virulence factor in enterobacteria, two distinct folds are found: an autoinhibited state in which its two protein domains strongly interact, and an active state in which these domains dissociate due to a specific DNA signal on RNA polymerases. This activation is accompanied by the refolding of the C-terminal domain (CTD) from an α-helical structure to a β-barrel. Our work employs computational simulations to explore the role of the N-terminal domain (NTD) in regulating the metamorphic behavior of RfaH, determining that this domain has a major part in orienting and binding to the CTD in its α-helical fold, and in stabilizing an intermediate state instead of the fully folded β-barrel. These results suggest that the NTD not only participates in stabilizing the autoinhibited state, but also aids in fold-switching back to it after active RfaH is released from RNA polymerase.

Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation

Amyloid peptides are known to self-assemble into larger aggregates that are linked to the pathogenesis of many neurodegenerative disorders. In contrast to primary nucleation, recent experimental and theoretical studies have shown that many toxic oligomeric species are generated through secondary processes on a pre-existing fibrillar surface. Nucleation, for example, can also occur along the surface of a pre-existing fibril—secondary nucleation—as opposed to the primary one. However, explicit pathways are still not clear. In this study, we use molecular dynamics simulation to explore the free energy landscape of a free Abeta monomer binding to an existing fibrillar surface. We specifically look into several potential Abeta structural precursors that might precede some secondary events, including elongation and secondary nucleation. We find that the overall process of surface-dependent events can be described at least by the following three stages: 1. Free diffusion 2. Downhill guiding 3. Dock and lock. And we show that the outcome of adding a new monomer onto a pre-existing fibril is pathway-dependent, which leads to different secondary processes. To understand structural details, we have identified several monomeric amyloid precursors over the fibrillar surfaces and characterize their heterogeneity using a probability contact map analysis. Using the frustration analysis (a bioinformatics tool), we show that surface heterogeneity correlates with the energy frustration of specific local residues that form binding sites on the fibrillar structure. We further investigate the helical twisting of protofilaments of different sizes and observe a length dependence on the filament twisting. This work presents a comprehensive survey over the properties of fibril growth using a combination of several openMM-based platforms, including the GPU-enabled openAWSEM package for coarse-grained modeling, MDTraj for trajectory analysis, and pyEMMA for free energy calculation. This combined approach makes long-timescale simulation for aggregation systems as well as all-in-one analysis feasible. We show that this protocol allows us to explore fibril stability, surface binding affinity/heterogeneity, as well as fibrillar twisting. All these properties are important for understanding the molecular mechanism of surface-catalyzed secondary processes of fibril growth.

Impact of Cu(II) and Al(III) on the conformational landscape of amyloidβ1-42

Metal ions have been found to play an important role in the formation of extracellular β-amyloid plaques, a major hallmark of Alzheimer's disease. In the present study, the conformational landscape of Aβ42 with Al(iii) and Cu(ii) has been explored using Gaussian accelerated molecular dynamics. Both metals reduce the flexibility of the peptide and entail a higher structural organization, although to different degrees. As a general trend, Cu(ii) binding leads to an increased α-helix content and to the formation of two α-helices that tend to organize in a U-shape. By contrast, most Al(iii) complexes induce a decrease in helical content, leading to more extended structures that favor the appearance of transitory β-strands.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

Mesoscopic protein-rich clusters host the nucleation of mutant p53 amyloid fibrils

The protein p53 is a crucial tumor suppressor, often called "the guardian of the genome"; however, mutations transform p53 into a powerful cancer promoter. The oncogenic capacity of mutant p53 has been ascribed to enhanced propensity to fibrillize and recruit other cancer fighting proteins in the fibrils, yet the pathways of fibril nucleation and growth remain obscure. Here, we combine immunofluorescence three-dimensional confocal microscopy of human breast cancer cells with light scattering and transmission electron microscopy of solutions of the purified protein and molecular simulations to illuminate the mechanisms of phase transformations across multiple length scales, from cellular to molecular. We report that the p53 mutant R248Q (R, arginine; Q, glutamine) forms, both in cancer cells and in solutions, a condensate with unique properties, mesoscopic protein-rich clusters. The clusters dramatically diverge from other protein condensates. The cluster sizes are decoupled from the total cluster population volume and independent of the p53 concentration and the solution concentration at equilibrium with the clusters varies. We demonstrate that the clusters carry out a crucial biological function: they host and facilitate the nucleation of amyloid fibrils. We demonstrate that the p53 clusters are driven by structural destabilization of the core domain and not by interactions of its extensive unstructured region, in contradistinction to the dense liquids typical of disordered and partially disordered proteins. Two-step nucleation of mutant p53 amyloids suggests means to control fibrillization and the associated pathologies through modifying the cluster characteristics. Our findings exemplify interactions between distinct protein phases that activate complex physicochemical mechanisms operating in biological systems.

Defining the Neuropathological Aggresome across in Silico, in Vitro, and ex Vivo Experiments

The loss of proteostasis over the life course is associated with a wide range of debilitating degenerative diseases and is a central hallmark of human aging. When left unchecked, proteins that are intrinsically disordered can pathologically aggregate into highly ordered fibrils, plaques, and tangles (termed amyloids), which are associated with countless disorders such as Alzheimer's disease, Parkinson's disease, type II diabetes, cancer, and even certain viral infections. However, despite significant advances in protein folding and solution biophysics techniques, determining the molecular cause of these conditions in humans has remained elusive. This has been due, in part, to recent discoveries showing that soluble protein oligomers, not insoluble fibrils or plaques, drive the majority of pathological processes. This has subsequently led researchers to focus instead on heterogeneous and often promiscuous protein oligomers. Unfortunately, significant gaps remain in how to prepare, model, experimentally corroborate, and extract amyloid oligomers relevant to human disease in a systematic manner. This Review will report on each of these techniques and their successes and shortcomings in an attempt to standardize comparisons between protein oligomers across disciplines, especially in the context of neurodegeneration. By standardizing multiple techniques and identifying their common overlap, a clearer picture of the soluble neuropathological aggresome can be constructed and used as a baseline for studying human disease and aging.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

The role of alpha-helix on the structure-targeting drug design of amyloidogenic proteins

The most accredited hypothesis links the toxicity of amyloid proteins to their harmful effects on membrane integrity through the formation of prefibrillar-transient oligomers able to disrupt cell membranes. However, damage mechanisms necessarily assume a first step in which the amyloidogenic protein transfers from the aqueous phase to the membrane hydrophobic core. This determinant step is still poorly understood. However, according to our lipid-chaperon hypothesis, free lipids in solution play a crucial role in facilitating this footfall. Free phospholipid concentration in the aqueous phase acts as a switch between ion channel-like pore and fibril formation, so that high free lipid concentration in solution promotes pore and repress fibril formation. Conversely, low free lipids in the solution favor fibril and repress pore formation. This behavior is due to the formation of stable lipid-protein complexes. Here, we hypothesize that the helix propensity is a fundamental requirement to fulfill the lipid-chaperon model. The alpha-helix region seems to be responsible for the binding with amphiphilic molecules fostering the proposed mechanism. Indeed, our results show the dependency of protein-lipid binding from the helical structure presence. When the helix content is substantially lower than the wild type, the contact probability decreases. Instead, if the helix is broadening, the contact probability increases. Our findings open a new perspective for in silico screening of secondary structure-targeting drugs of amyloidogenic proteins.

A theoretical study of polymorphism in VQIVYK fibrils

The VQIVYK fragment from the Tau protein, also known as PHF6, is essential for aggregation of Tau into neurofibrillary lesions associated with neurodegenerative diseases. VQIVYK itself forms amyloid fibrils composed of paired β-sheets. Therefore, the full Tau protein and VQIVYK fibrils have been intensively investigated. A central issue in these studies is polymorphism, the ability of a protein to fold into more than one structure. Using all-atom molecular simulations, we generate five stable polymorphs of VQIVYK fibrils, establish their relative free energy with umbrella sampling methods, and identify the side chain interactions that provide stability. The two most stable polymorphs, which have nearly equal free energy, are formed by interdigitation of the mostly hydrophobic VIY "face" sides of the β-sheets. Another stable polymorph is formed by interdigitation of the QVK "back" sides. When we turn to examine structures from cryo-electron microscopy experiments on Tau filaments taken from diseased patients or generated in vitro, we find that the pattern of side chain interactions found in the two most stable face-to-face as well as the back-to-back polymorphs are recapitulated in amyloid structures of the full protein. Thus, our studies suggest that the interactions stabilizing PHF6 fibrils explain the amyloidogenicity of the VQIVYK motif within the full Tau protein and provide justification for the use of VQIVYK fibrils as a test bed for the design of molecules that identify or inhibit amyloid structures.

β-Turn exchanges in the α-synuclein segment 44-TKEG-47 reveal high sequence fidelity requirements of amyloid fibril elongation

The folding of turns and β-hairpins has been implicated in amyloid formation, with diverse potential consequences such as promotion or inhibition of fibril nucleation, fibril elongation, or off-pathway oligomer formation. In the Parkinson's disease-associated protein α-synuclein (αS), a β-hairpin comprised of residues 36-56 was detected in complex with an engineered binding protein, with a turn formed by the αS sequence segment 44-TKEG-47. Molecular dynamics simulations revealed extensive populations of transient β-hairpin conformations in this region in free, monomeric αS. Here, we investigated potential effects of turn formation on αS fibril formation by studying the aggregation kinetics of an extensive set of αS variants with between two and four amino acid exchanges in the 44-TKEG-47 segment. The exchanges were chosen to specifically promote formation of β1-, β1'-, or β2'-turns. All variants assembled into amyloid fibrils, with increased β1'- or β2'-turn propensity associated with faster aggregation and increased β1-turn propensity with slower aggregation compared to wild-type (WT) αS. Atomic force microscopy demonstrated that β-turn exchanges altered fibril morphology. In cross-elongation experiments, the turn variants showed a low ability to elongate WT fibril seeds, and, vice versa, WT monomer did not efficiently elongate turn variant fibril seeds. This demonstrates that sequence identity in the turn region is crucial for efficient αS fibril elongation. Elongation experiments of WT fibril seeds in the presence of both WT and turn variant monomers suggest that the turn variants can bind and block WT fibril ends to different degrees, but cannot efficiently convert into the WT fibril structure. Our results indicate that modifications in the 44-TKEG-47 segment strongly affect amyloid assembly by driving αS into alternative fibril morphologies, whose elongation requires high sequence fidelity.