Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides

Role of hydrophobicity on self-assembly by peptide amphiphiles via molecular dynamics simulations

Using a novel coarse-grained model, large-scale molecular dynamics simulations were performed to examine self-assembly of 800 peptide amphiphiles (sequence palmitoyl-V3A3E3). Under suitable physiological conditions, these molecules readily assemble into nanofibers leading to hydrogel construction as observed in experiments. Our simulations capture this spontaneous self-assembly process, including formation of secondary structure, to identify morphological transitions of distinctive nanostructures. As the hydrophobic interaction is increased, progression from open networks of secondary structures toward closed cylindrical nanostructures containing either β-sheets or random coils are observed. Moreover, temperature effects are also determined to play an important role in regulating formation of secondary structures within those nanostructures. These understandings of the molecular interactions involved and the role of environmental factors on hydrogel formation provide useful insight for development of innovative smart biomaterials for biomedical applications.

Are current atomistic force fields accurate enough to study proteins in crowded environments?

The high concentration of macromolecules in the crowded cellular interior influences different thermodynamic and kinetic properties of proteins, including their structural stabilities, intermolecular binding affinities and enzymatic rates. Moreover, various structural biology methods, such as NMR or different spectroscopies, typically involve samples with relatively high protein concentration. Due to large sampling requirements, however, the accuracy of classical molecular dynamics (MD) simulations in capturing protein behavior at high concentration still remains largely untested. Here, we use explicit-solvent MD simulations and a total of 6.4 µs of simulated time to study wild-type (folded) and oxidatively damaged (unfolded) forms of villin headpiece at 6 mM and 9.2 mM protein concentration. We first perform an exhaustive set of simulations with multiple protein molecules in the simulation box using GROMOS 45a3 and 54a7 force fields together with different types of electrostatics treatment and solution ionic strengths. Surprisingly, the two villin headpiece variants exhibit similar aggregation behavior, despite the fact that their estimated aggregation propensities markedly differ. Importantly, regardless of the simulation protocol applied, wild-type villin headpiece consistently aggregates even under conditions at which it is experimentally known to be soluble. We demonstrate that aggregation is accompanied by a large decrease in the total potential energy, with not only hydrophobic, but also polar residues and backbone contributing substantially. The same effect is directly observed for two other major atomistic force fields (AMBER99SB-ILDN and CHARMM22-CMAP) as well as indirectly shown for additional two (AMBER94, OPLS-AAL), and is possibly due to a general overestimation of the potential energy of protein-protein interactions at the expense of water-water and water-protein interactions. Overall, our results suggest that current MD force fields may distort the picture of protein behavior in biologically relevant crowded environments.

Protein behavior is strongly affected by highly crowded and interaction-rich environments, i.e., typical conditions in both biologically relevant systems, such as the cellular interior, and solution-based structural experiments, including NMR and different spectroscopies. On the other hand, primarily because of limited computational power, molecular dynamics (MD) simulations, a premier high-resolution method for analyzing structure, dynamics and interactions of proteins, have been predominantly used to study individual proteins at infinite dilution. To fill this gap, we use MD simulations to study the behavior of wild-type (aggregation-resistant) and oxidatively damaged (aggregation-prone) forms of villin headpiece at high concentration, and reveal unexpected limitations and inaccuracies of modern-day MD force fields when it comes to modeling proteins at physiologically or experimentally relevant concentrations.

Protein accumulation in the endoplasmic reticulum as a non-equilibrium phase transition

Several neurological disorders are associated with the aggregation of aberrant proteins, often localized in intracellular organelles such as the endoplasmic reticulum. Here we study protein aggregation kinetics by mean-field reactions and three dimensional Monte carlo simulations of diffusion-limited aggregation of linear polymers in a confined space, representing the endoplasmic reticulum. By tuning the rates of protein production and degradation, we show that the system undergoes a non-equilibrium phase transition from a physiological phase with little or no polymer accumulation to a pathological phase characterized by persistent polymerization. A combination of external factors accumulating during the lifetime of a patient can thus slightly modify the phase transition control parameters, tipping the balance from a long symptomless lag phase to an accelerated pathological development. The model can be successfully used to interpret experimental data on amyloid-β clearance from the central nervous system.

Misfolded protein accumulation is a hallmark of many neurodegenerative diseases. Here Budrikis et al. model protein aggregation in the endoplasmic reticulum and show that it is the result of a non-equilibrium phase transition caused by tipping the balance from the rates of protein production to degradation.

Minimal model of self-assembly: emergence of diversity and complexity

Molecular self-assembly is ubiquitous in nature, yet prediction of assembly pathways from fundamental interparticle interactions has yet to be achieved. Here, we introduce a minimal self-assembly model with two attractive and two repulsive beads bound into a tetrahedron. The model is associated with a single parameter η defined as the repulsive to attractive interaction ratio. We explore self-assembly pathways and resulting assembly morphologies for different η values by discrete molecular dynamics. Our results demonstrate that η governs the assembly dynamics and resulting assembly morphologies, revealing an unexpected diversity and complexity for 0.5 ≤ η < 1. One of the key processes that governs the assembly dynamics is assembly breakage, which emerges spontaneously at η > 0 with the breakage rate increasing with η. The observed assembly pathways display a broad variety of assembly structures characteristic of aggregation of amyloidogenic proteins, including quasi-spherical oligomers that coassemble into elongated protofibrils, followed by a conversion into ordered polymorphic fibril-like aggregates. We further demonstrate that η can be meaningfully mapped onto amyloidogenic protein sequences, with the majority of amyloidogenic proteins characterized by 0.5 ≤ η < 1. Prion proteins, which are known to form highly breakage-prone fibrils, are characterized by η > 1, consistent with the model predictions. Our model thus provides a theoretical basis for understanding the universal aspects of aggregation pathways of amyloidogenic proteins relevant to human disease. As the model is not specific to proteins, these findings represent an important step toward understanding and predicting assembly dynamics of not only proteins but also viruses, colloids, and nanoparticles.

Surface-mediated protein disaggregation

Preventing protein aggregation is of both biological and industrial importance. Interprotein interactions between the hydrophobic residues of the protein are known to be the major driving force for protein aggregation. In this article, we show how surface chemistry and curvature can be tuned to mitigate these interprotein interactions. Our results calculated in the framework of the Hydrophobic-Polar (HP) lattice model show that interprotein interactions can be drastically reduced by increasing the surface hydrophobicity to a critical value corresponding to the adsorption transition of the protein. At this value of surface hydrophobicity, proteins lose interprotein contacts to gain surface contacts, and thus the surface helps to reduce the interprotein interactions. Furthermore, we show that the adsorption of the proteins inside hydrophobic pores of optimal sizes are most efficient at both reducing interprotein contacts and simultaneously retaining most of the native contacts probably as a result of confinement-induced stabilization.

Perspective: Coarse-grained models for biomolecular systems

By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.

Length-dependent β-sheet growth mechanisms of polyalanine peptides in water and on hydrophobic surfaces

Fibrillar assemblies by peptides are becoming one of the most promising nanomaterials due to their exceptional properties. The self-assembly of peptides into β sheets is a critical step in the fibrillization pathway. We investigated the length-dependent β-sheet growth mechanisms of polyalanine [poly(A)] peptides consisting of 6 to 24 alanines (A6 to A24) in water and on the hydrophobic surface, respectively, by molecular dynamics simulations. β-sheet growth behavior in water fits negative exponential growth model, showing that β-sheet growth rate decays exponentially with time. Meanwhile, increasing chain length leads to an accelerated decay of the β-sheet growth rate. By contrast, β-sheet growth on the surface from A6 to A18 occurs in two consecutive stages, both of which fit linear growth models. β-sheet growth rate in the first stage increases as chain length is increased, while the intermediate length peptide A12 has the highest β-sheet growth rate in the second stage. β-sheet growth behavior of A24 on the surface still fits negative exponential model. Overall, the hydrophobic surface accelerates β-sheet growth by enhancing local concentration and reducing conformational entropy of poly(A) peptide, and the β-sheet growth of the intermediate length peptide A12 is the fastest on the surface. Our simulation results shed light on understanding the accelerated peptide fibrillization on the hydrophobic surface.

Impact of sequence on the molecular assembly of short amyloid peptides

The goal of this work is to understand how the sequence of a protein affects the likelihood that it will form an amyloid fibril and the kinetics along the fibrillization pathway. The focus is on very short fragments of amyloid proteins since these play a role in the fibrillization of the parent protein and can form fibrils themselves. Discontinuous molecular dynamics simulations using the PRIME20 force field were performed of the aggregation of 48-peptide systems containing SNQNNF (PrP (170-175)), SSTSAA (RNaseA(15-20)), MVGGVV (Aβ(35-40)), GGVVIA (Aβ(37-42)), and MVGGVVIA (Aβ(35-42)). In our simulations SNQQNF, SSTTSAA, and MVGGVV form large numbers of fibrillar structures spontaneously (as in experiment). GGVVIA forms β-sheets that do not stack into fibrils (unlike experiment). The combination sequence MVGGVVIA forms less fibrils than MVGGVV, hindered by the presence of the hydrophobic residues at the C-terminal. Analysis of the simulation kinetics and energetics reveals why MVGGVV forms fibrils and GGVVIA does not, and why adding I and A to MVGGVVIA reduces fibrillization and enhances amorphous aggregation into oligomeric structures. The latter helps explain why Aβ(1-42) assembles into more complex oligomers than Aβ(1-40), a consequence of which is that it is more strongly associated with Alzheimer's disease.

A simple lattice model that captures protein folding, aggregation and amyloid formation

The ability of many proteins to convert from their functional soluble state to amyloid fibrils can be attributed to inter-molecular beta strand formation. Such amyloid formation is associated with neurodegenerative disorders like Alzheimer's and Parkinson's. Molecular modelling can play a key role in providing insight into the factors that make proteins prone to fibril formation. However, fully atomistic models are computationally too expensive to capture the length and time scales associated with fibril formation. As the ability to form fibrils is the rule rather than the exception, much insight can be gained from the study of coarse-grained models that capture the key generic features associated with amyloid formation. Here we present a simple lattice model that can capture both protein folding and beta strand formation. Unlike standard lattice models, this model explicitly incorporates the formation of hydrogen bonds and the directionality of side chains. The simplicity of our model makes it computationally feasible to investigate the interplay between folding, amorphous aggregation and fibril formation, and maintains the capability of classic lattice models to simulate protein folding with high specificity. In our model, the folded proteins contain structures that resemble naturally occurring beta-sheets, with alternating polar and hydrophobic amino acids. Moreover, fibrils with intermolecular cross-beta strand conformations can be formed spontaneously out of multiple short hydrophobic peptide sequences. Both the formation of hydrogen bonds in folded structures and in fibrils is strongly dependent on the amino acid sequence, indicating that hydrogen-bonding interactions alone are not strong enough to initiate the formation of beta sheets. This result agrees with experimental observations that beta sheet and amyloid formation is strongly sequence dependent, with hydrophobic sequences being more prone to form such structures. Our model should open the way to a systematic study of the interplay between the factors that lead to amyloid formation.

Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach

Maximum Likelihood (ML) optimization schemes are widely used for parameter inference. They maximize the likelihood of some experimentally observed data, with respect to the model parameters iteratively, following the gradient of the logarithm of the likelihood. Here, we employ a ML inference scheme to infer a generalizable, physics-based coarse-grained protein model (which includes Go̅-like biasing terms to stabilize secondary structure elements in room-temperature simulations), using native conformations of a training set of proteins as the observed data. Contrastive divergence, a novel statistical machine learning technique, is used to efficiently approximate the direction of the gradient ascent, which enables the use of a large training set of proteins. Unlike previous work, the generalizability of the protein model allows the folding of peptides and a protein (protein G) which are not part of the training set. We compare the same force field with different van der Waals (vdW) potential forms: a hard cutoff model, and a Lennard-Jones (LJ) potential with vdW parameters inferred or adopted from the CHARMM or AMBER force fields. Simulations of peptides and protein G show that the LJ model with inferred parameters outperforms the hard cutoff potential, which is consistent with previous observations. Simulations using the LJ potential with inferred vdW parameters also outperforms the protein models with adopted vdW parameter values, demonstrating that model parameters generally cannot be used with force fields with different energy functions. The software is available at https://sites.google.com/site/crankite/.

Dynamics of an intrinsically disordered protein reveal metastable conformations that potentially seed aggregation

Amyloid fibril deposits of the intrinsically disordered hIAPP peptide are found in 95% of type II diabetes patients, and the aggregation of this peptide is suggested to induce apoptotic cell-death in insulin-producing β-cells. Understanding the structure and dynamics of the hIAPP monomer in solution is thus important for understanding the nucleation of aggregation and the formation of oligomers. In this study, we identify the metastable conformational states of the hIAPP monomer and the dynamics of transitioning between them using Markov state models constructed from extensive molecular dynamics simulations. We show that the overall structure of the hIAPP peptide is random coil-like and lacks a dominant folded structure. Despite this fact, our model reveals a large number of reasonably well-populated metastable conformational states (or local free energy minima) having populations of a few percent or less. The time scales for transitioning between these states range from several microseconds to milliseconds. In contrast to folded proteins, there is no kinetic hub. More strikingly, a few states contain significant amounts of β-hairpin secondary structure and extended hydrophobic surfaces that are exposed to the solvent. We propose that these states may facilitate the nucleation of hIAPP aggregation through a significant component of the conformational selection mechanism, because they may increase their populations upon aggregation by promoting hydrophobic interactions and at the same time provide a flat geometry to seed the ordered β-strand packing of the fibrils.

The role of electrostatics and temperature on morphological transitions of hydrogel nanostructures self-assembled by peptide amphiphiles via molecular dynamics simulations

Smart biomaterials that are self-assembled from peptide amphiphiles (PA) are known to undergo morphological transitions in response to specific physiological stimuli. The design of such customizable hydrogels is of significant interest due to their potential applications in tissue engineering, biomedical imaging, and drug delivery. Using a novel coarse-grained peptide/polymer model, which has been validated by comparison of equilibrium conformations from atomistic simulations, large-scale molecular dynamics simulations are performed to examine the spontaneous self-assembly process. Starting from initial random configurations, these simulations result in the formation of nanostructures of various sizes and shapes as a function of the electrostatics and temperature. At optimal conditions, the self-assembly mechanism for the formation of cylindrical nanofibers is deciphered involving a series of steps: (1) PA molecules quickly undergo micellization whose driving force is the hydrophobic interactions between alkyl tails; (2) neighboring peptide residues within a micelle engage in a slow ordering process that leads to the formation of β-sheets exposing the hydrophobic core; (3) spherical micelles merge together through an end-to-end mechanism to form cylindrical nanofibers that exhibit high structural fidelity to the proposed structure based on experimental data. As the temperature and electrostatics vary, PA molecules undergo alternative kinetic mechanisms, resulting in the formation of a wide spectrum of nanostructures. A phase diagram in the electrostatics-temperature plane is constructed delineating regions of morphological transitions in response to external stimuli.

Misfolding and amyloid aggregation of apomyoglobin

Apomyoglobin is an excellent example of a monomeric all α-helical globular protein whose folding pathway has been extensively studied and well characterized. Structural perturbation induced by denaturants or high temperature as well as amino acid substitution have been described to induce misfolding and, in some cases, aggregation. In this article, we review the molecular mechanism of the aggregation process through which a misfolded form of a mutated apomyoglobin aggregates at physiological pH and room temperature forming an amyloid fibril. The results are compared with data showing that either amyloid or aggregate formation occurs under particular denaturing conditions or upon cleavage of the residues corresponding to the C-terminal helix of apomyoglobin. The results are discussed in terms of the sequence regions that are more important than others in determining the amyloid aggregation process.

Coarse-Grained Models for Protein-Cell Membrane Interactions

The physiological properties of biological soft matter are the product of collective interactions, which span many time and length scales. Recent computational modeling efforts have helped illuminate experiments that characterize the ways in which proteins modulate membrane physics. Linking these models across time and length scales in a multiscale model explains how atomistic information propagates to larger scales. This paper reviews continuum modeling and coarse-grained molecular dynamics methods, which connect atomistic simulations and single-molecule experiments with the observed microscopic or mesoscale properties of soft-matter systems essential to our understanding of cells, particularly those involved in sculpting and remodeling cell membranes.

Modelling and analysis of early aggregation events of BMHP1-derived self-assembling peptides

Despite the increasing use and development of peptide-based scaffolds in different fields including that of regenerative medicine, the understanding of the factors governing the self-assembly process and the relationship between sequence and properties have not yet been fully understood. BMHP1-derived self-assembling peptides (SAPs) have been developed and characterized showing that biotinylation at the N-terminal cap corresponds to better performing assembly and scaffold biomechanics. In this study, the effects of biotinylation on the self-assembly dynamics of seven BMHP1-derived SAPs have been investigated by molecular dynamics simulations. We confirmed that these SAPs self-assemble into β-structures and that proline acts as a β-breaker of the assembled aggregates. In biotinylated peptides, the formation of ordered β-structured aggregates is triggered by both the establishment of a dense and dynamic H-bonds network and the formation of a 'hydrophobic wall' available to interact with other peptides. Such conditions result from the peculiar chemical composition of the biotinyl-cap, given by the synergic cooperation of the uracil function of the ureido ring with the high hydrophobic portion consisting of the thiophenyl ring and valeryl chain. The inbuilt propensity of biotinylated peptides towards the formation of ordered small aggregates makes them ideal precursors of higher hierarchically organized self-assembled nanostructures as experimentally observed.

Conformational ensemble and polymorphism of the all-atom Alzheimer's Aβ(37-42) amyloid peptide oligomers

Although the Aβ(37-42) peptide has two opposite terminal charges, counterintuitively its current fibril amyloid structure reveals in register parallel β-strands, as formed by the full length Aβ peptide. In this study, we carried out a replica exchange molecular dynamics simulation of 16 all-atom Aβ(37-42) peptides in explicit water starting from randomized and dispersed chains. The extensive conformational sampling (48 replicas, 460 ns/replica) with a total simulation time of 23 μs allows us to obtain a full picture on the equilibrium conformational distribution of oligomers and β-sheet sizes and gain some insights into the oligomerization process at 300 K. At the peptide concentration of 12 mM, self-assembly is described by the condensation-polymerization mechanism with conversion from micelle-like to high β-sheet structures. At equilibrium, the oligomer distribution consists of large aggregates and free monomers, representing 70% and 25% of all species, respectively. Though the formation/dissociation of β-strand is high, the population of 4-5 fully parallel β-strands, consistent with the arrangement in the current fibril, is marginal and that of 4-5 fully antiparallel β-strands, consistent with amyloid polymorphism, is non-negligible. However, the system adopts essentially mixed parallel/antiparallel β-strands. This indicates that a system of 16 Aβ(37-42) chains in explicit solvent still does not form more stable species that will irreversibly grow to a fibril, independently of polymorphism. Our results also suggest that the Aβ(37-42) fibril may display packing polymorphism with antiparallel β-strands, in addition to the experimentally observed in register parallel β-strands.

Molecular dynamics simulations of DPPC bilayers using "LIME", a new coarse-grained model

A new intermediate resolution model for phospholipids, LIME, designed for use with discontinuous molecular dynamics (DMD) simulations is presented. The implicit-solvent model was developed using a multiscale modeling approach in which the geometric and energetic parameters are obtained by collecting data from atomistic simulations of a system composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) molecules and explicit water. In the model, 14 coarse-grained sites that are classified as 1 of 6 types represent DPPC. DMD simulations performed on a random solution of DPPC resulted in the formation of a defect-free bilayer in less than 4 h. The bilayer formed quantitatively reproduces the main structural properties (e.g., area per lipid, bilayer thickness, bond order parameters) that are observed experimentally. In addition, the bilayer transitions from a liquid-crystalline phase to a tilted gel phase when the temperature is reduced. Transbilayer movement of a lipid from the bottom leaflet to the top leaflet is observed when the temperature is increased.

β-sheet propensity controls the kinetic pathways and morphologies of seeded peptide aggregation

The effect of seeds in templating the morphology of peptide aggregates is examined using molecular dynamics simulations and a coarse-grained peptide representation. Varying the nature of the aggregate seed between β-sheet, amorphous, and β-barrel seeds leads to different aggregation pathways and to morphologically different aggregates. Similar effects are seen by varying the β-sheet propensity of the free peptides. For a fibrillar seed and free peptides of high β-sheet propensity, fibrillar growth occurred by means of direct attachment (without structural rearrangement) of free individual peptides and small ordered oligomers onto the seed. For a fibrillar seed and free peptides of low β-sheet propensity, fibrillar growth occurred through a dock-lock mechanism, in which the free peptides first docked onto the seed, and then locked on, extending and aligning to join the fibril. Amorphous seeds absorbed free peptides into themselves indiscriminately, with any fibrillar rearrangement subsequent to this absorption by means of a condensation-ordering transition. Although the mechanisms observed by varying peptide β-sheet propensity are diverse, the initial pathways can always be broken down into the following steps: (i) the free peptides diffuse in the bulk and attach individually to the seed; (ii) the free peptides diffuse and aggregate among themselves; (iii) the free peptide oligomers collide with the seed; and (iv) the free oligomers merge with the seed and rearrange in a manner dependent on the backbone flexibility of both the free and seed peptides. Our simulations indicate that it is possible to sequester peptides from amorphous aggregates into fibrils, and also that aggregate morphology (and thus cytoxicity) can be controlled by introducing seeds of aggregate-compatible peptides with differing β-sheet propensities into the system.

Reduced atomic pair-interaction design (RAPID) model for simulations of proteins

Increasingly, theoretical studies of proteins focus on large systems. This trend demands the development of computational models that are fast, to overcome the growing complexity, and accurate, to capture the physically relevant features. To address this demand, we introduce a protein model that uses all-atom architecture to ensure the highest level of chemical detail while employing effective pair potentials to represent the effect of solvent to achieve the maximum speed. The effective potentials are derived for amino acid residues based on the condition that the solvent-free model matches the relevant pair-distribution functions observed in explicit solvent simulations. As a test, the model is applied to alanine polypeptides. For the chain with 10 amino acid residues, the model is found to reproduce properly the native state and its population. Small discrepancies are observed for other folding properties and can be attributed to the approximations inherent in the model. The transferability of the generated effective potentials is investigated in simulations of a longer peptide with 25 residues. A minimal set of potentials is identified that leads to qualitatively correct results in comparison with the explicit solvent simulations. Further tests, conducted for multiple peptide chains, show that the transferable model correctly reproduces the experimentally observed tendency of polyalanines to aggregate into β-sheets more strongly with the growing length of the peptide chain. Taken together, the reported results suggest that the proposed model could be used to succesfully simulate folding and aggregation of small peptides in atomic detail. Further tests are needed to assess the strengths and limitations of the model more thoroughly.

Molecular pathology of polyalanine expansion disorders: new perspectives from mouse models

Disease-causing polyalanine (PA) expansion mutations have been identified in nine genes, eight of which encode transcription factors (TFs) with important roles in development. In vitro and cell overexpression studies have shown that expanded PA tracts result in protein misfolding and the formation of aggregates. This feature of PA proteins is reminiscent of the related polyglutamine (PQ) disease proteins, which have been shown to cause disease via a gain-of-function (GOF) mechanism. However, in sharp contrast to PQ disorders, the disease phenotypes associated with PA mutations are more consistent with a LOF and/or mild GOF mechanism, suggesting that their molecular pathology is inherently different to PQ disorders. Elucidating the cellular impact of PA mutations in vivo has been difficult to address as, unlike the late-onset polyglutamine disorders, all PA disorders associated with TF gene mutations are congenital. However, in recent years, significant advances have been made through the analysis of engineered (knock-in) and spontaneous PA mouse models. Here we review these recent findings and propose an updated model of the molecular and cellular mechanism of PA disorders that incorporates both LOF and GOF features.

Efficient Relaxation of Protein-Protein Interfaces by Discrete Molecular Dynamics Simulations

Protein-protein interactions are responsible for the transfer of information inside the cell and represent one of the most interesting research fields in structural biology. Unfortunately, after decades of intense research, experimental approaches still have difficulties in providing 3D structures for the hundreds of thousands of interactions formed between the different proteins in a living organism. The use of theoretical approaches like docking aims to complement experimental efforts to represent the structure of the protein interactome. However, we cannot ignore that current methods have limitations due to problems of sampling of the protein-protein conformational space and the lack of accuracy of available force fields. Cases that are especially difficult for prediction are those in which complex formation implies a non-negligible change in the conformation of the interacting proteins, i.e., those cases where protein flexibility plays a key role in protein-protein docking. In this work, we present a new approach to treat flexibility in docking by global structural relaxation based on ultrafast discrete molecular dynamics. On a standard benchmark of protein complexes, the method provides a general improvement over the results obtained by rigid docking. The method is especially efficient in cases with large conformational changes upon binding, in which structure relaxation with discrete molecular dynamics leads to a predictive success rate double that obtained with state-of-the-art rigid-body docking.

Keampferol-3-O-rhamnoside abrogates amyloid beta toxicity by modulating monomers and remodeling oligomers and fibrils to non-toxic aggregates

BACKGROUND

Aggregation of soluble, monomeric β- amyloid (Aβ) to oligomeric and then insoluble fibrillar Aβ is a key pathogenic feature in development of Alzheimer's disease (AD). Increasing evidence suggests that toxicity is linked to diffusible Aβ oligomers, rather than to insoluble fibrils. The use of naturally occurring small molecules for inhibition of Aβ aggregation has recently attracted significant interest for development of effective therapeutic strategies against the disease. A natural polyphenolic flavone, Kaempferol-3-O-rhamnoside (K-3-rh), was utilized to investigate its effects on aggregation and cytotoxic effects of Aβ42 peptide. Several biochemical techniques were used to determine the conformational changes and cytotoxic effect of the peptide in the presence and absence of K-3-rh.

RESULTS

K-3-rh showed a dose-dependent effect against Aβ42 mediated cytotoxicity. Anti-amyloidogenic properties of K-3-rh were found to be efficient in inhibiting fibrilogenesis and secondary structural transformation of the peptide. The consequence of these inhibitions was the accumulation of oligomeric structural species. The accumulated aggregates were smaller, soluble, non-β-sheet and non-toxic aggregates, compared to preformed toxic Aβ oligomers. K-3-rh was also found to have the remodeling properties of preformed soluble oligomers and fibrils. Both of these conformers were found to remodel into non-toxic aggregates. The results showed that K-3-rh interacts with different Aβ conformers, which affects fibril formation, oligomeric maturation and fibrillar stabilization.

CONCLUSION

K-3-rh is an efficient molecule to hinder the self assembly and to abrogate the cytotoxic effects of Aβ42 peptide. Hence, K-3-rh and small molecules with similar structure might be considered for therapeutic development against AD.

Digital and analog chemical evolution

Living matter is the most elaborate, elegant, and complex hierarchical material known and is consequently the natural target for an ever-expanding scientific and technological effort to unlock and deconvolute its marvelous forms and functions. Our current understanding suggests that biological materials are derived from a bottom-up process, a spontaneous emergence of molecular networks in the course of chemical evolution. Polymer cooperation, so beautifully manifested in the ribosome, appeared in these dynamic networks, and the special physicochemical properties of the nucleic and amino acid polymers made possible the critical threshold for the emergence of extant cellular life. These properties include the precise and geometrically discrete hydrogen bonding patterns that dominate the complementary interactions of nucleic acid base-pairing that guide replication and ensure replication fidelity. In contrast, complex and highly context-dependent sets of intra- and intermolecular interactions guide protein folding. These diverse interactions allow the more analog environmental chemical potential fluctuations to dictate conformational template-directed propagation. When these two different strategies converged in the remarkable synergistic ribonucleoprotein that is the ribosome, this resulting molecular digital-to-analog converter achieved the capacity for both persistent information storage and adaptive responses to an ever-changing environment. The ancestral chemical networks that preceded the Central Dogma of Earth's biology must reflect the dynamic chemical evolutionary landscapes that allowed for selection, propagation, and diversification and ultimately the demarcation and specialization of function that modern biopolymers manifest. Not only should modern biopolymers contain molecular fossils of this earlier age, but it should be possible to use this information to reinvent these dynamic functional networks. In this Account, we review the first dynamic network created by modification of a nucleic acid backbone and show how it has exploited the digital-like base pairing for reversible polymer construction and information transfer. We further review how these lessons have been extended to the complex folding landscapes of templated peptide assembly. These insights have allowed for the construction of molecular hybrids of each biopolymer class and made possible the reimagining of chemical evolution. Such elaboration of biopolymer chimeras has already led to applications in therapeutics and diagnostics, to the construction of novel nanostructured materials, and toward orthogonal biochemical pathways that expand the evolution of existing biochemical systems. The ability to look beyond the primordial emergence of the ribosome may allow us to better define the origins of chemical evolution, to extend its horizons beyond the biology of today and ask whether evolution is an inherent property of matter unbounded by physical limitations imposed by our planet's diverse environments.

Natural biomolecules and protein aggregation: emerging strategies against amyloidogenesis

Biomolecular self-assembly is a fundamental process in all organisms. As primary components of the life molecular machinery, proteins have a vast array of resources available to them for self-assembly in a functional structure. Protein self-assembly, however, can also occur in an aberrant way, giving rise to non-native aggregated structures responsible for severe, progressive human diseases that have a serious social impact. Different neurodegenerative disorders, like Huntington's, Alzheimer's, and spongiform encephalopathy diseases, have in common the presence of insoluble protein aggregates, generally termed "amyloid," that share several physicochemical features: a fibrillar morphology, a predominantly beta-sheet secondary structure, birefringence upon staining with the dye Congo red, insolubility in common solvents and detergents, and protease resistance. Conformational constrains, hydrophobic and stacking interactions can play a key role in the fibrillogenesis process and protein-protein and peptide-peptide interactions-resulting in self-assembly phenomena of peptides yielding fibrils-that can be modulated and influenced by natural biomolecules. Small organic molecules, which possess both hydrophilic and hydrophobic moieties able to bind to peptide/protein molecules through hydrogen bonds and hydrophobic and aromatic interactions, are potential candidates against amyloidogenesis. In this review some significant case examples will be critically discussed.

Kinetics of amyloid aggregation: a study of the GNNQQNY prion sequence

The small amyloid-forming GNNQQNY fragment of the prion sequence has been the subject of extensive experimental and numerical studies over the last few years. Using unbiased molecular dynamics with the OPEP coarse-grained potential, we focus here on the onset of aggregation in a 20-mer system. With a total of 16.9 of simulations at 280 K and 300 K, we show that the GNNQQNY aggregation follows the classical nucleation theory (CNT) in that the number of monomers in the aggregate is a very reliable descriptor of aggregation. We find that the critical nucleus size in this finite-size system is between 4 and 5 monomers at 280 K and 5 and 6 at 300 K, in overall agreement with experiment. The kinetics of growth cannot be fully accounted for by the CNT, however. For example, we observe considerable rearrangements after the nucleus is formed, as the system attempts to optimize its organization. We also clearly identify two large families of structures that are selected at the onset of aggregation demonstrating the presence of well-defined polymorphism, a signature of amyloid growth, already in the 20-mer aggregate.

Protein aggregation plays an important pathological role in numerous neurodegenerative diseases such as Alzheimer's, Parkinson's, Creutzfeldt-Jakob, the Prion disease and diabetes mellitus. In most cases, misfolded proteins are involved and aggregate irreversibly to form highly ordered insoluble macrostructures, called amyloid fibrils, which deposit in the brain. Studies have revealed that all proteins are capable of forming amyloid fibrils that all share common structural features and therefore aggregation mechanisms. The toxicity of amyloid aggregates is however not attributed to the fibrils themselves but rather to smaller more disordered aggregates, oligomers, forming parallel to or prior to fibrils. Understanding the assembly process of these amyloid oligomers is key to understanding their toxicity mechanism in order to devise a possible treatment strategy targeting these toxic aggregates. Our approach here is to computationally study the aggregation dynamics of a 20-mer of an amyloid peptide GNNQQNY from a prion protein. Our findings suggest that the assembly is a spontaneous process that can be described as a complex nucleation and growth mechanism and which can lead to two classes of morphologies for the aggregates, one of which resembles a protofibril-like structure. Such numerical studies are crucial to understanding the details of fast biological processes and complement well experimental studies.

Influence of temperature on formation of perfect tau fragment fibrils using PRIME20/DMD simulations

We investigate the fibrillization process for amyloid tau fragment peptides (VQIVYK) by applying the discontinuous molecular dynamics method to a system of 48 VQIVYK peptides modeled using a new protein model/force field, PRIME20. The aim of the article is to ascertain which factors are most important in determining whether or not a peptide system forms perfect coherent fibrillar structures. Two different directional criteria are used to determine when a hydrogen bond occurs: the original H-bond constraints and a parallel preference H-bond constraint that imparts a slight bias towards the formation of parallel versus antiparallel strands in a β-sheet. Under the original H-bond constraints, the resulting fibrillar structures contain a mixture of parallel and antiparallel pairs of strands within each β-sheet over the whole fibrillization temperature range. Under the parallel preference H-bond constraints, the β-sheets within the fibrillar structures are more likely to be parallel and indeed become perfectly parallel, consistent with X-ray crystallography, at a high temperature slightly below the fibrillization temperature. The high temperature environment encourages the formation of perfect fibril structures by providing enough time and space for peptides to rearrange during the aggregation process. There are two different kinetic mechanisms, template assembly with monomer addition at high temperature and merging/rearrangement without monomer addition at low temperature, which lead to significant differences in the final fibrillar structure. This suggests that the diverse fibril morphologies generally observed in vitro depend more on environmental conditions than has heretofore been appreciated.

Concentration and temperature dependences of polyglutamine aggregation by multiscale coarse-graining molecular dynamics simulations

The solvent-free multiscale coarse-graining model of polyglutamine was employed to study polyglutamine aggregation at different concentrations and temperatures by means of molecular dynamics simulation. The heterogeneity order parameter (HOP) was used to quantify the polyglutamine aggregation. Our simulation results demonstrate that polyglutamine aggregation is sensitive to concentration and temperature changes. In equilibrium states, polyglutamine molecules fluctuate between aggregating tightly and distributing uniformly. The degree of aggregation monotonically increases with decreasing temperature, but the fluctuation of HOP reaches its maximum at an intermediate temperature. With increasing concentration, the distribution of polyglutamines first changes from more uniform to more nonuniform and then changes back to be more uniform, and the HOP has the widest distribution at the turning point. Simulations with different system sizes indicate that the finite-size effect is trivial and do not change the conclusions drawn for the polyglutamine system. In addition, the composition of the potential energies has been analyzed to confirm that the nonbonded interactions dominate the aggregation of polyglutamines. These results can be thermodynamically understood by considering the competition between the system entropy and molecular interactions, and a statistical model based on HOP has been developed to explain the microscopic mechanism of polyglutamine aggregation.

Measurements of 13C multiple-quantum coherences in amyloid fibrils under magic-angle spinning

The excitation and detection of high-order multiple quantum coherences among (13)C nuclear spins are demonstrated in the samples of [1-(13)C]-L-alanine and (13)C labeled amyloid fibrils at a spinning frequency of 20 kHz. The technique is based on the double-quantum average Hamiltonian prepared by the DRAMA-XY4 pulse sequence. Empirically, we find that multiple supercycles are required to suppress the higher-order effects for real applications. Measurements for the fibril samples formed by the polypeptides of PrP(113-127) provide the first solid-state NMR evidence for the stacking of multiple β-sheet layers at the structural core of amyloid fibrils.

Structural transitions and oligomerization along polyalanine fibril formation pathways from computer simulations

The results of a computer simulation study of the aggregation kinetics of a large system of model peptides with particular focus on the formation of intermediates are presented. Discontinuous molecular dynamic simulations were used in combination with our intermediate-resolution protein model, PRIME, to simulate the aggregation of a system of 192 polyalanine (KA(14) K) peptides at a concentration of 5 mM and a reduced temperature of T* = 0.13 starting from a random configuration and ending in the assembly of a fibrillar structure. The population of various structures, including free monomers, beta sheets, amorphous aggregates, hybrid aggregates, and fibrils, and the transitions between the structures were tracked over the course of 30 independent simulations and averaged together. The aggregation pathway for this system starts with the association of free monomers into small amorphous aggregates that then grow to moderate size by incorporating other free monomers or merging with other small amorphous aggregates. These then rearrange into either small beta sheets or hybrid aggregates formed by association between unstructured chains and beta sheets, both of which grow in size by adding free monomer chains or other small aggregates, one at a time. Fibrillar structures are formed initially either by the stacking of beta sheets, rearrangement of hybrid aggregates or association between beta sheets and hybrid aggregates. They grow by the addition of beta sheets, hybrid aggregates, and other small fibrillar structures. The rearrangement of amorphous aggregates into beta sheets is a critical and necessary step in the fibril formation pathway.

Insight into the stability of cross-β amyloid fibril from VEALYL short peptide with molecular dynamics simulation

Amyloid fibrils are found in many fatal neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, type II diabetes, and prion disease. The VEALYL short peptide from insulin has been confirmed to aggregate amyloid-like fibrils. However, the aggregation mechanism of amyloid fibril is poorly understood. Here, we utilized molecular dynamics simulation to analyse the stability of VEALYL hexamer. The statistical results indicate that hydrophobic residues play key roles in stabilizing VEALYL hexamer. Single point and two linkage mutants confirmed that Val1, Leu4, and Tyr5 of VEALYL are key residues. The consistency of the results for the VEALYL oligomer suggests that the intermediate states might be trimer (3-0) and pentamer(3-2). These results can help us to obtain an insight into the aggregation mechanism of amyloid fibril. These methods can be used to study the stability of amyloid fibril from other short peptides.

Phase networks of cross-β peptide assemblies

Recent evidence suggests that simple peptides can access diverse amphiphilic phases, and that these structures underlie the robust and widely distributed assemblies implicated in nearly 40 protein misfolding diseases. Here we exploit a minimal nucleating core of the Aβ peptide of Alzheimer's disease to map its morphologically accessible phases that include stable intermolecular molten particles, fibers, twisted and helical ribbons, and nanotubes. Analyses with both fluorescence lifetime imaging microscopy (FLIM) and transmission electron microscopy provide evidence for liquid-liquid phase separations, similar to the coexisting dilute and dense protein-rich liquid phases so critical for the liquid-solid transition in protein crystallization. We show that the observed particles are critical for transitions to the more ordered cross-β peptide phases, which are prevalent in all amyloid assemblies, and identify specific conditions that arrest assembly at the phase boundaries. We have identified a size dependence of the particles in order to transition to the para-crystalline phase and a width of the cross-β assemblies that defines the transition between twisted fibers and helically coiled ribbons. These experimental results reveal an interconnected network of increasing molecularly ordered cross-β transitions, greatly extending the initial computational models for cross-β assemblies.

Temporal relationship of autophagy and apoptosis in neurons challenged by low molecular weight β-amyloid peptide

Alzheimer's disease (AD) is an aging-related progressive neurodegenerative disorder. Previous studies suggested that various soluble Aβ species are neurotoxic and able to activate apoptosis and autophagy, the type I and type II programmed cell death, respectively. However, the sequential and functional relationships between these two cellular events remain elusive. Here we report that low molecular weight Aβ triggered cleavage of caspase 3 and poly (ADP-ribose) polymerase to cause neuronal apoptosis in rat cortical neurons. On the other hand, Aβ activated autophagy by inducing autophagic vesicle formation and autophagy related gene 12 (ATG12), and up-regulated the lysoso-mal machinery for the degradation of autophagosomes. Moreover, we demonstrated that activation of autophagy by Aβ preceded that of apoptosis, with death associated protein kinase phosphorylation as the potential molecular link. More importantly, under Aβ toxicity, neurons exhibiting high level of autophagosome formation were absent of apoptotic features, and inhibition of autophagy by 3-methylade-nine advanced neuronal apoptosis, suggesting that autophagy can protect neurons from Aβ-induced apoptosis.

Studying interactions by molecular dynamics simulations at high concentration

Molecular dynamics simulations have been used to study molecular encounters and recognition. In recent works, simulations using high concentration of interacting molecules have been performed. In this paper, we consider the practical problems for setting up the simulation and to analyse the results of the simulation. The simulation of beta 2-microglobulin association and the simulation of the binding of hydrogen peroxide by glutathione peroxidase are provided as examples.

Role of water in protein aggregation and amyloid polymorphism

A variety of neurodegenerative diseases are associated with amyloid plaques, which begin as soluble protein oligomers but develop into amyloid fibrils. Our incomplete understanding of this process underscores the need to decipher the principles governing protein aggregation. Mechanisms of in vivo amyloid formation involve a number of coconspirators and complex interactions with membranes. Nevertheless, understanding the biophysical basis of simpler in vitro amyloid formation is considered important for discovering ligands that preferentially bind regions harboring amyloidogenic tendencies. The determination of the fibril structure of many peptides has set the stage for probing the dynamics of oligomer formation and amyloid growth through computer simulations. Most experimental and simulation studies, however, have been interpreted largely from the perspective of proteins: the role of solvent has been relatively overlooked in oligomer formation and assembly to protofilaments and amyloid fibrils. In this Account, we provide a perspective on how interactions with water affect folding landscapes of amyloid beta (Aβ) monomers, oligomer formation in the Aβ16-22 fragment, and protofilament formation in a peptide from yeast prion Sup35. Explicit molecular dynamics simulations illustrate how water controls the self-assembly of higher order structures, providing a structural basis for understanding the kinetics of oligomer and fibril growth. Simulations show that monomers of Aβ peptides sample a number of compact conformations. The formation of aggregation-prone structures (N*) with a salt bridge, strikingly similar to the structure in the fibril, requires overcoming a high desolvation barrier. In general, sequences for which N* structures are not significantly populated are unlikely to aggregate. Oligomers and fibrils generally form in two steps. First, water is expelled from the region between peptides rich in hydrophobic residues (for example, Aβ16-22), resulting in disordered oligomers. Then the peptides align along a preferred axis to form ordered structures with anti-parallel β-strand arrangement. The rate-limiting step in the ordered assembly is the rearrangement of the peptides within a confining volume. The mechanism of protofilament formation in a polar peptide fragment from the yeast prion, in which the two sheets are packed against each other and create a dry interface, illustrates that water dramatically slows self-assembly. As the sheets approach each other, two perfectly ordered one-dimensional water wires form. They are stabilized by hydrogen bonds to the amide groups of the polar side chains, resulting in the formation of long-lived metastable structures. Release of trapped water from the pore creates a helically twisted protofilament with a dry interface. Similarly, the driving force for addition of a solvated monomer to a preformed fibril is water release; the entropy gain and favorable interpeptide hydrogen bond formation compensate for entropy loss in the peptides. We conclude by offering evidence that a two-step model, similar to that postulated for protein crystallization, must also hold for higher order amyloid structure formation starting from N*. Distinct water-laden polymorphic structures result from multiple N* structures. Water plays multifarious roles in all of these protein aggregations. In predominantly hydrophobic sequences, water accelerates fibril formation. In contrast, water-stabilized metastable intermediates dramatically slow fibril growth rates in hydrophilic sequences.

Oligomeric intermediates in amyloid formation: structure determination and mechanisms of toxicity

Oligomeric intermediates are non-fibrillar polypeptide assemblies that occur during amyloid fibril formation and that are thought to underlie the aetiology of amyloid diseases, such as Alzheimer's disease, Parkinson's disease and Huntington's disease. Focusing primarily on the oligomeric states formed from Alzheimer's disease β-amyloid (Aβ) peptide, this review will make references to other polypeptide systems, highlighting common principles or sequence-specific differences. The covered topics include the structural properties and polymorphism of oligomers, the biophysical mechanism of peptide self-assembly and its role for pathogenicity in amyloid disease. Oligomer-dependent toxicity mechanisms will be explained along with recently emerging possibilities of interference.

Coarse-grained simulations of protein aggregation

Protein aggregation is believed to be responsible for a number of human diseases and limits the yields of pharmaceutical proteins during production. Computer simulations can be used to develop novel experimentally testable hypotheses pertaining to aggregation. While all-atom simulations with explicit solvent are too computationally intensive to address the multitude of relevant time scales, coarse-grained models make it possible to observe the transition of monomers to an equilibrium containing aggregates. Here, we provide the reader with background information and a list of steps for setting up, performing, and analyzing computer simulations of aggregating coarse-grained (CG) proteins.

Fibrillization propensity for short designed hexapeptides predicted by computer simulation

Assembly of normally soluble proteins into ordered aggregates, known as amyloid fibrils, is a cause or associated symptom of numerous human disorders, including Alzheimer's and the prion diseases. Here, we test the ability of discontinuous molecular dynamics (DMD) simulations based on PRIME20, a new intermediate-resolution protein force field, to predict which designed hexapeptide sequences will form fibrils, which will not, and how this depends on temperature and concentration. Simulations were performed on 48-peptide systems containing STVIIE, STVIFE, STVIVE, STAIIE, STVIAE, STVIGE, and STVIEE starting from random-coil configurations. By the end of the simulations, STVIIE and STVIFE (which form fibrils in vitro) form fibrils over a range of temperatures, STVIEE (which does not form fibrils in vitro) does not form fibrils, and STVIVE, STAIIE, STVIAE, and STVIGE (which do not form fibrils in vitro) form fibrils at lower temperatures but stop forming fibrils at higher temperatures. At the highest temperatures simulated, the results on the fibrillization propensity of the seven short de novo designed peptides all agree with the experiments of López de la Paz and Serrano. Our results suggest that the fibrillization temperature (temperature above which fibrils cease to form) is a measure of fibril stability and that by rank ordering the fibrillization temperatures of various sequences, PRIME20/DMD simulations could be used to ascertain their relative fibrillization propensities. A phase diagram showing regions in the temperature-concentration plane where fibrils are formed in our simulations is presented.