Investigating how peptide length and a pathogenic mutation modify the structural ensemble of amyloid beta monomer

Comparison of force fields for Alzheimer's A β42: A case study for intrinsically disordered proteins

Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Aβ42 , an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields.

The folding mechanism and key metastable state identification of the PrP127–147 monomer studied by molecular dynamics simulations and Markov state model analysis

MD simulation combined with MSM analysis was employed to investigate the structural dynamics and the folding mechanism of the key fragment 127–147 monomer of prion protein.

Exploiting computationally derived out-of-the-box protein conformations for drug design

Structural plasticity is an intrinsic property of proteins that allows each gene product to accomplish its tasks in a strictly regulated manner at a precise time and cellular location. Moreover, protein motions allow protein-ligand and protein-protein recognition. The knowledge of the conformational ensemble that a drug target populates may be crucial for the design of small molecules that can differently modulate its function. X-ray crystallography and NMR have endlessly provided snapshots of protein states. However, experimental structure determination is not always straightforward. Therefore, attempts have been made to depict protein conformational landscapes through molecular dynamics and enhanced sampling methods. Here, we review how accounting for protein dynamics through in silico generated out-of-the-box protein conformations has started to impact on drug discovery.

Monomer Dynamics of Alzheimer Peptides and Kinetic Control of Early Aggregation in Alzheimer's Disease

The rate of reconfiguration-or intramolecular diffusion-of monomeric Alzheimer (Aβ) peptides is measured and, under conditions that aggregation is more likely, peptide diffusion slows down significantly, which allows bimolecular associations to be initiated. By using the method of Trp-Cys contact quenching, the rate of reconfiguration is observed to be about five times faster for Aβ₄₀ , which aggregates slowly, than that for Aβ₄₂ , which aggregates quickly. Furthermore, the rate of reconfiguration for Aβ₄₂ speeds up at higher pH, which slows aggregation, and in the presence of the aggregation inhibitor curcumin. The measured reconfiguration rates are able to predict the early aggregation behavior of the Aβ peptide and provide a kinetic basis for why Aβ₄₂ is more prone to aggregation than Aβ₄₀ , despite a difference of only two amino acids.

Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking

MDM2 is a negative regulator of p53 activity and an important target for cancer therapeutics. The N-terminal lid region of MDM2 modulates interactions with p53 via competition for its binding cleft, exchanging slowly between docked and undocked conformations in the absence of p53. To better understand these dynamics, we constructed Markov State Models (MSMs) from large collections of unbiased simulation trajectories of apo-MDM2, and find strong evidence for diffuse, yet two-state folding and binding of the N-terminal region to the p53 receptor site. The MSM also identifies holo-like receptor conformations highly suitable for computational docking, despite initiating trajectories from closed-cleft receptor structures unsuitable for docking. Fixed-anchor docking studies using a test set of high-affinity small molecules and peptides show simulated receptor ensembles achieve docking successes comparable to cross-docking studies using crystal structures of receptors bound by alternative ligands. For p53, the best-scoring receptor structures have the N-terminal region lid region bound in a helical conformation mimicking the bound structure of p53, suggesting lid region association induces receptor conformations suitable for binding. These results suggest that MD + MSM approaches can sample binding-competent receptor conformations suitable for computational peptidomimetic design, and that inclusion of disordered regions may be essential to capturing the correct receptor dynamics.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Advances in the Simulation of Protein Aggregation at the Atomistic Scale

Protein aggregation into highly structured amyloid fibrils is associated with various diseases including Alzheimer's disease, Parkinson's disease, and type II diabetes. Amyloids can also have normal biological functions and, in the future, could be used as the basis for novel nanoscale materials. However, a full understanding of the physicochemical forces that drive protein aggregation is still lacking. Such understanding is crucial for the development of drugs that can effectively inhibit aberrant amyloid aggregation and for the directed design of functional amyloids. Atomistic simulations can help understand protein aggregation. In particular, atomistic simulations can be used to study the initial formation of toxic oligomers which are hard to characterize experimentally and to understand the difference in aggregation behavior between different amyloidogenic peptides. Here, we review the latest atomistic simulations of protein aggregation, concentrating on amyloidogenic protein fragments, and provide an outlook for the future in this field.

All-atom molecular dynamics analysis of multi-peptide systems reproduces peptide solubility in line with experimental observations

In order to investigate the contribution of individual amino acids to protein and peptide solubility, we carried out 100 ns molecular dynamics (MD) simulations of 10⁶ ų cubic boxes containing ~3 × 10⁴ water molecules and 27 tetra-peptides regularly positioned at 23 Å from each other and composed of a single amino acid type for all natural amino acids but cysteine and glycine. The calculations were performed using Amber with a standard force field on a special purpose MDGRAPE-3 computer, without introducing any “artificial” hydrophobic interactions. Tetra-peptides composed of I, V, L, M, N, Q, F, W, Y, and H formed large amorphous clusters, and those containing A, P, S, and T formed smaller ones. Tetra-peptides made of D, E, K, and R did not cluster at all. These observations correlated well with experimental solubility tendencies as well as hydrophobicity scales with correlation coefficients of 0.5 to > 0.9. Repulsive Coulomb interactions were dominant in ensuring high solubility, whereas both Coulomb and van der Waals (vdW) energies contributed to the aggregations of low solubility amino acids. Overall, this very first all-atom molecular dynamics simulation of a multi-peptide system appears to reproduce the basic properties of peptide solubility, essentially in line with experimental observations.

Perturbations in inter-domain associations may trigger the onset of pathogenic transformations in PrP(C): insights from atomistic simulations

Conversion of the predominantly α-helical cellular prion protein (PrP(C)) to the misfolded β-sheet enriched Scrapie form (PrP(Sc)) is a critical event in prion pathogenesis. However, the conformational triggers that lead to the isoform conversion (PrP(C) to PrP(Sc)) remain obscure, and conjectures about the role of unusually hydrophilic, short helix H1 of the C-terminal globular domain in the transition are varied. Helix H1 is anchored to helix H3 via a few stabilizing polar interactions. We have employed fully atomistic molecular dynamics simulations to study the effects triggered by a minor perturbation in the network of these non-bonded interactions in PrP(C). The elimination of just one of the key H1-H3 hydrogen bonds led to a cascade of conformational changes that are consistent with those observed in partially unfolded intermediates of PrP(C), with pathogenic mutations and in low pH environments. Our analyses reveal that the perturbation results in the enhanced conformational flexibility of the protein. The resultant enhancement in the dynamics leads to overall increased solvent exposure of the hydrophobic core residues and concomitant disruption of the H1-H3 inter-domain salt bridge network. This study lends credence to the hypothesis that perturbing the cooperativity of the stabilizing interactions in the PrP(C) globular domain can critically affect its dynamics and may lead to structural transitions of pathological relevance.

Conformational features of the Aβ42 peptide monomer and its interaction with the surrounding solvent

Heterogeneous conformational flexibility of the Aβ monomers has been found to be correlated with the corresponding non-uniform entropy gains.

Dynamics of the conformational transitions during the dimerization of an intrinsically disordered peptide: a case study on the human islet amyloid polypeptide fragment

Dimerization pathways of the human islet amyloid polypeptide fragment are elucidated from extensive molecular dynamics simulations.

Characterization of Aβ Monomers through the Convergence of Ensemble Properties among Simulations with Multiple Force Fields

Amyloid β (Aβ) monomers represent a base state in the pathways of aggregation that result in the fibrils and oligomers implicated in the pathogenesis of Alzheimer's disease (AD). The structural properties of these intrinsically disordered peptides remain unclear despite extensive experimental and computational investigations. Further, there are mutations within Aβ that change the way the peptide aggregates and are known to cause familial AD (FAD). Here, we analyze the ensembles of different isoforms (Aβ42 and Aβ40) and mutants (E22Δ, D23N, E22K, E22G, and A2T in Aβ40) of Aβ generated with all-atom replica exchange molecular dynamics (REMD) simulations on the μs/replica time scale. These were run using three different force field/water model combinations: OPLS-AA/L and TIP3P ("OPLS"), AMBER99sb-ILDN and TIP4P-Ew ("ILDN"), as well as CHARMM22* and TIP3SP ("CHARMM"). Despite fundamental changes in simulation parameters, we find that the resulting ensembles demonstrate a strong convergence in structural properties. In particular, antiparallel contacts between L17-A21 and A30-L34 are prevalent in ensembles of Aβ40, directly forming β sheets in the OPLS and ILDN combinations. A21-A30 commonly forms an interceding region that rarely interacts with the rest of the peptide. Further, Aβ42 contributes new β hairpin motifs involving V40-I41 in both OPLS and ILDN. However, the structural flexibility of the central region and the electrostatic interactions that characterize it are notably different between the different conditions. Further, for OPLS, each of the FAD mutations disrupts central bend character and increases the polymorphism of antiparallel contacts across the central region. However, the studied mutations in the ILDN set primarily encourage more global contacts involving the N-terminus and the central region, and promote the formation of new β topologies that may seed different aggregates involved in disease phenotypes. These differences aside, the large degree of agreement between simulation sets across multiple force fields provides a generalizable characterization of Aβ that is also consistent with experimental data and models.

Alzheimer's protective A2T mutation changes the conformational landscape of the Aβ₁₋₄₂ monomer differently than does the A2V mutation

The aggregation of amyloid-β (Aβ) peptides plays a crucial role in the etiology of Alzheimer's disease (AD). Recently, it has been reported that an A2T mutation in Aβ can protect against AD. Interestingly, a nonpolar A2V mutation also has been found to offer protection against AD in the heterozygous state, although it causes early-onset AD in homozygous carriers. Since the conformational landscape of the Aβ monomer is known to directly contribute to the early-stage aggregation mechanism, it is important to characterize the effects of the A2T and A2V mutations on Aβ₁₋₄₂ monomer structure. Here, we have performed extensive atomistic replica-exchange molecular dynamics simulations of the solvated wild-type (WT), A2V, and A2T Aβ₁₋₄₂ monomers. Our simulations reveal that although all three variants remain as collapsed coils in solution, there exist significant structural differences among them at shorter timescales. A2V exhibits an enhanced double-hairpin population in comparison to the WT, similar to those reported in toxic WT Aβ₁₋₄₂ oligomers. Such double-hairpin formation is caused by hydrophobic clustering between the N-terminus and the central and C-terminal hydrophobic patches. In contrast, the A2T mutation causes the N-terminus to engage in unusual electrostatic interactions with distant residues, such as K16 and E22, resulting in a unique population comprising only the C-terminal hairpin. These findings imply that a single A2X (where X = V or T) mutation in the primarily disordered N-terminus of the Aβ₁₋₄₂ monomer can dramatically alter the β-hairpin population and switch the equilibrium toward alternative structures. The atomistically detailed, comparative view of the structural landscapes of A2V and A2T variant monomers obtained in this study can enhance our understanding of the mechanistic differences in their early-stage aggregation.

Exploring the Alzheimer amyloid-β peptide conformational ensemble: A review of molecular dynamics approaches

Alzheimer's disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat effectively this disease. One main reason is due to the poorly understood mechanism of Aβ peptide aggregation, which plays a crucial role in the development of Alzheimer's disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the Aβ monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of Aβ peptide revealed and characterized by computational approaches in order to find converging structures of Aβ monomer. Understanding how Aβ peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer's disease.

Varied Probability of Staying Collapsed/Extended at the Conformational Equilibrium of Monomeric Aβ40 and Aβ42

In present study, we set out to investigate the conformation dynamics of Aβ40 and Aβ42 through exploring the impact of intra-molecular interactions on conformation dynamics using equilibrium molecular dynamics simulations. Our 40 microsecond-scale simulations reveal heterogeneous conformation ensembles of Aβ40 and Aβ42 that encompass ~35% β-strand and ~60% unstructured coils. Two conformational states were identified in both alloforms: a collapsed state (CS) that resembles the structural motif of face-to-face hydrophobic clustering in amyloid fibrils, and an extended state (ES) that features the structural characteristics of anti-parallel β-sheets in amyloid oligomers. In Aβ40, the C-terminus remains unstructured and rarely interacts with other parts, thereof the hydrophobic clustering is in loose contact and the peptide assumes ES with high probability. In contrast, the C-terminus of Aβ42 adopts a β-strand structure that strongly interacts with segments E3-R5 and V18-A21. The active association leads to a more compact hydrophobic collapse and refrain the alloform from ES. Based on the structural characterization, we propose that the fibril and oligomer assembly pathways could respectively take off from CS and ES, and their aggregation propensity may be governed by the probability of visiting the corresponding conformational states at the equilibrium.

Evaluation of conformational changes in diabetes-associated mutation in insulin a chain: a molecular dynamics study

Insulin plays a central role in the regulation of metabolism in humans. Mutations in the insulin gene can impair the folding of its precursor protein, proinsulin, and cause permanent neonatal-onset diabetes mellitus known as Mutant INS-gene induced Diabetes of Youth (MIDY) with insulin deficiency. To gain insights into the molecular basis of this diabetes-associated mutation, we perform molecular dynamics simulations in wild-type and mutant (Cys(A7) to Tyr or C(A7)Y) insulin A chain in aqueous solutions. The C(A7)Y mutation is one of the identified mutations that impairs the protein folding by substituting the cysteine residue which is required for the disulfide bond formation. A comparative analysis reveals structural differences between the wild-type and the mutant conformations. The analyzed mutant insulin A chain forms a metastable state with major effects on its N-terminal region. This suggests that MIDY mutant involves formation of a partially folded intermediate with conformational change in N-terminal region in A chain that generates flexible N-terminal domain. This may lead to the abnormal interactions with other proinsulins in the aggregation process.

Markov state models provide insights into dynamic modulation of protein function

Protein function is inextricably linked to protein dynamics. As we move from a static structural picture to a dynamic ensemble view of protein structure and function, novel computational paradigms are required for observing and understanding conformational dynamics of proteins and its functional implications. In principle, molecular dynamics simulations can provide the time evolution of atomistic models of proteins, but the long time scales associated with functional dynamics make it difficult to observe rare dynamical transitions. The issue of extracting essential functional components of protein dynamics from noisy simulation data presents another set of challenges in obtaining an unbiased understanding of protein motions. Therefore, a methodology that provides a statistical framework for efficient sampling and a human-readable view of the key aspects of functional dynamics from data analysis is required. The Markov state model (MSM), which has recently become popular worldwide for studying protein dynamics, is an example of such a framework.

In this Account, we review the use of Markov state models for efficient sampling of the hierarchy of time scales associated with protein dynamics, automatic identification of key conformational states, and the degrees of freedom associated with slow dynamical processes. Applications of MSMs for studying long time scale phenomena such as activation mechanisms of cellular signaling proteins has yielded novel insights into protein function. In particular, from MSMs built using large-scale simulations of GPCRs and kinases, we have shown that complex conformational changes in proteins can be described in terms of structural changes in key structural motifs or “molecular switches” within the protein, the transitions between functionally active and inactive states of proteins proceed via multiple pathways, and ligand or substrate binding modulates the flux through these pathways. Finally, MSMs also provide a theoretical toolbox for studying the effect of nonequilibrium perturbations on conformational dynamics. Considering that protein dynamics in vivo occur under nonequilibrium conditions, MSMs coupled with nonequilibrium statistical mechanics provide a way to connect cellular components to their functional environments. Nonequilibrium perturbations of protein folding MSMs reveal the presence of dynamically frozen glass-like states in their conformational landscape. These frozen states are also observed to be rich in β-sheets, which indicates their possible role in the nucleation of β-sheet rich aggregates such as those observed in amyloid-fibril formation. Finally, we describe how MSMs have been used to understand the dynamical behavior of intrinsically disordered proteins such as amyloid-β, human islet amyloid polypeptide, and p53. While certainly not a panacea for studying functional dynamics, MSMs provide a rigorous theoretical foundation for understanding complex entropically dominated processes and a convenient lens for viewing protein motions.

Aβ self-association and adsorption on a hydrophobic nanosurface: competitive effects and the detection of small oligomers via electrical response

Treatment of Alzheimer's disease (AD) is impeded by the lack of effective early diagnostic methods. Small, soluble Aβ globulomers play a major role in AD neurotoxicity, and detecting their presence in aqueous fluids could lead to suitable sensors. We evaluate the adsorption behavior of small Aβ oligomers on the surface of a single walled carbon nanotube of high curvature. While the intrinsic self-assembly propensity of Aβ is markedly hindered by adsorption, the oligomeric units show high degrees of surface immobilization. Immobilized complexes are capable of oligomeric growth, but with a shifted monomer-oligomer equilibrium compared to the free states. In the presence of an ionic solution and suitable external electric fields, magnitudes of the current blockades are found to be sensitive to the oligomeric number of the adsorbed complex. However, this sensitivity gradually diminishes with increasing oligomeric size. The results provide a proof-of-concept basis for further investigations in the design of sensors for detecting the toxic small oligomers of Aβ.

Energetic contributions of residues to the formation of early amyloid-β oligomers

Low-weight amyloid-β (Aβ) oligomers formed at early stages of oligomerization rather than fibril assemblies seem to be the toxic components that drive neurodegeneration in Alzheimer's disease. Unfortunately, detailed knowledge of the structure of these early oligomers at the residue level is not yet available. In this study, we performed all-atom explicit solvent molecular dynamics simulations to examine the oligomerization process of Aβ10-35 monomers when forming dimers, trimers, tetramers and octamers, with four independent simulations of a total simulated time of 3 μs for each oligomer system. The decomposition of the stability free energy by MM-GBSA methodology allowed us to unravel the network of energetic interactions that stabilize such oligomers. The contribution of the intermonomeric van der Waals term is the most significant energy feature of the oligomerization process, consistent with the so-called hydrophobic effect. Furthermore, the decomposition of the stability free energy into residues and residue-pairwise terms revealed that it is mainly apolar interactions between the three specific hydrophobic fragments 31-35 (C-terminal region), 17-20 (central hydrophobic core) and 12-14 (N-terminal region) that are responsible for such a favourable effect. The conformation in which the hydrophobic cthr-chc interaction is oriented perpendicularly is particularly important. We propose three other model substructures that favour the oligomerization process and can thus be considered as molecular targets for future inhibitors. Understanding Aβ oligomerization at the residue level could lead to more efficient design of inhibitors of this process.

Disordered amyloidogenic peptides may insert into the membrane and assemble into common cyclic structural motifs

Aggregation of disordered amyloidogenic peptides into oligomers is the causative agent of amyloid-related diseases. In solution, disordered protein states are characterized by heterogeneous ensembles. Among these, β-rich conformers self-assemble via a conformational selection mechanism to form energetically-favored cross-β structures, regardless of their precise sequences. These disordered peptides can also penetrate the membrane, and electrophysiological data indicate that they form ion-conducting channels. Based on these and additional data, including imaging and molecular dynamics simulations of a range of amyloid peptides, Alzheimer's amyloid-β (Aβ) peptide, its disease-related variants with point mutations and N-terminal truncated species, other amyloidogenic peptides, as well as a cytolytic peptide and a synthetic gel-forming peptide, we suggest that disordered amyloidogenic peptides can also present a common motif in the membrane. The motif consists of curved, moon-like β-rich oligomers associated into annular organizations. The motif is favored in the lipid bilayer since it permits hydrophobic side chains to face and interact with the membrane and the charged/polar residues to face the solvated channel pores. Such channels are toxic since their pores allow uncontrolled leakage of ions into/out of the cell, destabilizing cellular ionic homeostasis. Here we detail Aβ, whose aggregation is associated with Alzheimer's disease (AD) and for which there are the most abundant data. AD is a protein misfolding disease characterized by a build-up of Aβ peptide as senile plaques, neurodegeneration, and memory loss. Excessively produced Aβ peptides may directly induce cellular toxicity, even without the involvement of membrane receptors through Aβ peptide-plasma membrane interactions.

Cross dimerization of amyloid-β and αsynuclein proteins in aqueous environment: a molecular dynamics simulations study

Self-assembly of the intrinsically unstructured proteins, amyloid beta (Aβ) and alpha synclein (αSyn), are associated with Alzheimer's Disease, and Parkinson's and Lewy Body Diseases, respectively. Importantly, pathological overlaps between these neurodegenerative diseases, and the possibilities of interactions between Aβ and αSyn in biological milieu emerge from several recent clinical reports and in vitro studies. Nevertheless, there are very few molecular level studies that have probed the nature of spontaneous interactions between these two sequentially dissimilar proteins and key characteristics of the resulting cross complexes. In this study, we have used atomistic molecular dynamics simulations to probe the possibility of cross dimerization between αSyn1-95 and Aβ1-42, and thereby gain insights into their plausible early assembly pathways in aqueous environment. Our analyses indicate a strong probability of association between the two sequences, with inter-protein attractive electrostatic interactions playing dominant roles. Principal component analysis revealed significant heterogeneity in the strength and nature of the associations in the key interaction modes. In most, the interactions of repeating Lys residues, mainly in the imperfect repeats 'KTKEGV' present in αSyn1-95 were found to be essential for cross interactions and formation of inter-protein salt bridges. Additionally, a hydrophobicity driven interaction mode devoid of salt bridges, where the non-amyloid component (NAC) region of αSyn1-95 came in contact with the hydrophobic core of Aβ1-42 was observed. The existence of such hetero complexes, and therefore hetero assembly pathways may lead to polymorphic aggregates with variations in pathological attributes. Our results provide a perspective on development of therapeutic strategies for preventing pathogenic interactions between these proteins.

Markov state models of biomolecular conformational dynamics

It has recently become practical to construct Markov state models (MSMs) that reproduce the long-time statistical conformational dynamics of biomolecules using data from molecular dynamics simulations. MSMs can predict both stationary and kinetic quantities on long timescales (e.g. milliseconds) using a set of atomistic molecular dynamics simulations that are individually much shorter, thus addressing the well-known sampling problem in molecular dynamics simulation. In addition to providing predictive quantitative models, MSMs greatly facilitate both the extraction of insight into biomolecular mechanism (such as folding and functional dynamics) and quantitative comparison with single-molecule and ensemble kinetics experiments. A variety of methodological advances and software packages now bring the construction of these models closer to routine practice. Here, we review recent progress in this field, considering theoretical and methodological advances, new software tools, and recent applications of these approaches in several domains of biochemistry and biophysics, commenting on remaining challenges.

Atomic and dynamic insights into the beneficial effect of the 1,4-naphthoquinon-2-yl-L-tryptophan inhibitor on Alzheimer's Aβ1-42 dimer in terms of aggregation and toxicity

Aggregation of the amyloid β protein (Aβ) peptide with 40 or 42 residues is one key feature in Alzheimer's disease (AD). The 1,4-naphthoquinon-2-yl-L-tryptophan (NQTrp) molecule was reported to alter Aβ self-assembly and reduce toxicity. Though nuclear magnetic resonance experiments and various simulations provided atomic information about the interaction of NQTrp with Aβ peptides spanning the regions of residues 12-28 and 17-42, none of these studies were conducted on the full-length Aβ1-42 peptide. To this end, we performed extensive atomistic replica exchange molecular dynamics simulations of Aβ1-42 dimer with two NQTrp molecules in explicit solvent, by using a force field known to fold diverse proteins correctly. The interactions between NQTrp and Aβ1-42, which change the Aβ interface by reducing most of the intermolecular contacts, are found to be very dynamic and multiple, leading to many transient binding sites. The most favorable binding residues are Arg5, Asp7, Tyr10, His13, Lys16, Lys18, Phe19/Phe20, and Leu34/Met35, providing therefore a completely different picture from in vitro and in silico experiments with NQTrp with shorter Aβ fragments. Importantly, the 10 hot residues that we identified explain the beneficial effect of NQTrp in reducing both the level of Aβ1-42 aggregation and toxicity. Our results also indicate that there is room to design more efficient drugs targeting Aβ1-42 dimer against AD.

Differences in β-strand populations of monomeric Aβ40 and Aβ42

Using homonuclear (1)H NOESY spectra, with chemical shifts, (3)JH(N)H(α) scalar couplings, residual dipolar couplings, and (1)H-(15)N NOEs, we have optimized and validated the conformational ensembles of the amyloid-β 1-40 (Aβ40) and amyloid-β 1-42 (Aβ42) peptides generated by molecular dynamics simulations. We find that both peptides have a diverse set of secondary structure elements including turns, helices, and antiparallel and parallel β-strands. The most significant difference in the structural ensembles of the two peptides is the type of β-hairpins and β-strands they populate. We find that Aβ42 forms a major antiparallel β-hairpin involving the central hydrophobic cluster residues (16-21) with residues 29-36, compatible with known amyloid fibril forming regions, whereas Aβ40 forms an alternative but less populated antiparallel β-hairpin between the central hydrophobic cluster and residues 9-13, that sometimes forms a β-sheet by association with residues 35-37. Furthermore, we show that the two additional C-terminal residues of Aβ42, in particular Ile-41, directly control the differences in the β-strand content found between the Aβ40 and Aβ42 structural ensembles. Integrating the experimental and theoretical evidence accumulated over the last decade, it is now possible to present monomeric structural ensembles of Aβ40 and Aβ42 consistent with available information that produce a plausible molecular basis for why Aβ42 exhibits greater fibrillization rates than Aβ40.

Conformational distribution and α-helix to β-sheet transition of human amylin fragment dimer

Experiments suggested that the fibrillation of the 11-25 fragment (hIAPP(11-25)) of human islet amyloid polypeptide (hIAPP or amylin) involves the formation of transient α-helical intermediates, followed by conversion to β-sheet-rich structure. However, atomic details of α-helical intermediates and the transition mechanism are mostly unknown. We investigated the structural properties of the monomer and dimer in atomistic detail by replica exchange molecular dynamics (REMD) simulations. Transient α-helical monomers and dimers were both observed in the REMD trajectories. Our calculated H(α) chemical shifts based on the monomer REMD run are in agreement with the solution-state NMR experimental observations. Multiple 300 ns MD simulations at 310 K show that α-helix-to-β-sheet transition follows two mechanisms: the first involved direct transition of the random coil part of the helical conformation into antiparallel β-sheet, and in the second, the α-helical conformation unfolded and converted into antiparallel β-sheet. In both mechanisms, the α-helix-to-β-sheet transition occurred via random coil, and the transition was accompanied by an increase of interpeptide contacts. In addition, our REMD simulations revealed different temperature dependencies of helical and β-structures. Comparison with experimental data suggests that the propensity for hIAPP(11-25) to form α-helices and amyloid structures is concentration- and temperature-dependent.

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.

Comparative studies of disordered proteins with similar sequences: application to Aβ40 and Aβ42

Quantitative comparisons of intrinsically disordered proteins (IDPs) with similar sequences, such as mutant forms of the same protein, may provide insights into IDP aggregation-a process that plays a role in several neurodegenerative disorders. Here we describe an approach for modeling IDPs with similar sequences that simplifies the comparison of the ensembles by utilizing a single library of structures. The relative population weights of the structures are estimated using a Bayesian formalism, which provides measures of uncertainty in the resulting ensembles. We applied this approach to the comparison of ensembles for Aβ40 and Aβ42. Bayesian hypothesis testing finds that although both Aβ species sample β-rich conformations in solution that may represent prefibrillar intermediates, the probability that Aβ42 samples these prefibrillar states is roughly an order of magnitude larger than the frequency in which Aβ40 samples such structures. Moreover, the structure of the soluble prefibrillar state in our ensembles is similar to the experimentally determined structure of Aβ that has been implicated as an intermediate in the aggregation pathway. Overall, our approach for comparative studies of IDPs with similar sequences provides a platform for future studies on the effect of mutations on the structure and function of disordered proteins.

Systematic improvement of a classical molecular model of water

We report the iAMOEBA ("inexpensive AMOEBA") classical polarizable water model. The iAMOEBA model uses a direct approximation to describe electronic polarizability, in which the induced dipoles are determined directly from the permanent multipole electric fields and do not interact with one another. The direct approximation reduces the computational cost relative to a fully self-consistent polarizable model such as AMOEBA. The model is parameterized using ForceBalance, a systematic optimization method that simultaneously utilizes training data from experimental measurements and high-level ab initio calculations. We show that iAMOEBA is a highly accurate model for water in the solid, liquid, and gas phases, with the ability to fully capture the effects of electronic polarization and predict a comprehensive set of water properties beyond the training data set including the phase diagram. The increased accuracy of iAMOEBA over the fully polarizable AMOEBA model demonstrates ForceBalance as a method that allows the researcher to systematically improve empirical models by efficiently utilizing the available data.

Discrete molecular dynamics study of oligomer formation by N-terminally truncated amyloid β-protein

In Alzheimer's disease (AD), amyloid β-protein (Aβ) self-assembles into toxic oligomers. Of the two predominant Aβ alloforms, Aβ1-40 and Aβ1-42, the latter is particularly strongly linked to AD. N-terminally truncated and pyroglutamated Aβ peptides were recently shown to seed Aβ aggregation and contribute significantly to Aβ-mediated toxicity, yet their folding and assembly were not explored computationally. Discrete molecular dynamics approach previously captured in vitro-derived distinct Aβ1-40 and Aβ1-42 oligomer size distributions and predicted that the more toxic Aβ1-42 oligomers had more flexible and solvent-exposed N-termini than Aβ1-40 oligomers. Here, we examined oligomer formation of Aβ3-40, Aβ3-42, Aβ11-40, and Aβ11-42 by the discrete molecular dynamics approach. The four N-terminally truncated peptides showed increased oligomerization propensity relative to the full-length peptides, consistent with in vitro findings. Conformations formed by Aβ3-40/42 had significantly more flexible and solvent-exposed N-termini than Aβ1-40/42 conformations. In contrast, in Aβ11-40/42 conformations, the N-termini formed more contacts and were less accessible to the solvent. The compactness of the Aβ11-40/42 conformations was in part facilitated by Val12. Two single amino acid substitutions that reduced and abolished hydrophobicity at position 12, respectively, resulted in a proportionally increased structural variability. Our results suggest that Aβ11-40 and Aβ11-42 oligomers might be less toxic than Aβ1-40 and Aβ1-42 oligomers and offer a plausible explanation for the experimentally observed increased toxicity of Aβ3-40 and Aβ3-42 and their pyroglutamated forms.

Aβ monomers transiently sample oligomer and fibril-like configurations: ensemble characterization using a combined MD/NMR approach

Amyloid β (Aβ) peptides are a primary component of fibrils and oligomers implicated in the etiology of Alzheimer's disease (AD). However, the intrinsic flexibility of these peptides has frustrated efforts to investigate the secondary and tertiary structure of Aβ monomers, whose conformational landscapes directly contribute to the kinetics and thermodynamics of Aβ aggregation. In this work, de novo replica exchange molecular dynamics (REMD) simulations on the microseconds-per-replica timescale are used to characterize the structural ensembles of Aβ42, Aβ40, and M35-oxidized Aβ42, three physiologically relevant isoforms with substantially different aggregation properties. J-coupling data calculated from the REMD trajectories were compared to corresponding NMR-derived values acquired through two different pulse sequences, revealing that all simulations converge on the order of hundreds of nanoseconds-per-replica toward ensembles that yield good agreement with experiment. Though all three Aβ species adopt highly heterogeneous ensembles, these are considerably more structured compared to simulations on shorter timescales. Prominent in the C-terminus are antiparallel β-hairpins between L17-A21, A30-L36, and V39-I41, similar to oligomer and fibril intrapeptide models that expose these hydrophobic side chains to solvent and may serve as hotspots for self-association. Compared to reduced Aβ42, the absence of a second β-hairpin in Aβ40 and the sampling of alternate β topologies by M35-oxidized Aβ42 may explain the reduced aggregation rates of these forms. A persistent V24-K28 bend motif, observed in all three species, is stabilized by buried backbone to side-chain hydrogen bonds with D23 and a cross-region salt bridge between E22 and K28, highlighting the role of the familial AD-linked E22 and D23 residues in Aβ monomer folding. These characterizations help illustrate the conformational landscapes of Aβ monomers at atomic resolution and provide insight into the early stages of Aβ aggregation pathways.

Learning Kinetic Distance Metrics for Markov State Models of Protein Conformational Dynamics

Statistical modeling of long timescale dynamics with Markov state models (MSMs) has been shown to be an effective strategy for building quantitative and qualitative insight into protein folding processes. Existing methodologies, however, rely on geometric clustering using distance metrics such as root mean square deviation (RMSD), assuming that geometric similarity provides an adequate basis for the kinetic partitioning of phase space. Here, inspired by advances in the machine learning community, we introduce a new approach for learning a distance metric explicitly constructed to model kinetic similarity. This approach enables the construction of models, especially in the regime of high anisotropy in the diffusion constant, with fewer states than was previously possible. Application of this technique to the analysis of two ultralong molecular dynamics simulations of the FiP35 WW domain identifies discrete near-native relaxation dynamics in the millisecond regime that were not resolved in previous analyses.

Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9

Markov State Models (MSMs) provide an automated framework to investigate the dynamical properties of high-dimensional molecular simulations. These models can provide a human-comprehensible picture of the underlying process, and have been successfully used to study protein folding, protein aggregation, protein ligand binding, and other biophysical systems. The MSM requires the construction of a discrete state-space such that two points are in the same state if they can interconvert rapidly. In the following, we suggest an improved method, which utilizes second order Independent Components Analysis (also known as time-structure based Independent Components Analysis, or tICA), to construct the state-space. We apply this method to simulations of NTL9 (provided by Lindorff-Larsen et al. Science2011), and show that the MSM is an improvement over previously built models using conventional distance metrics. Additionally, the resulting model provides insight into the role of non-native contacts by revealing many slow timescales associated with compact, non-native states.

Effects of familial mutations on the monomer structure of Aβ₄₂

Amyloid beta (Aβ) peptide plays an important role in Alzheimer's disease. A number of mutations in the Aβ sequence lead to familial Alzheimer's disease, congophilic amyloid angiopathy, or hereditary cerebral hemorrhage with amyloid. Using molecular dynamics simulations of ∼200 μs for each system, we characterize and contrast the consequences of four pathogenic mutations (Italian, Dutch, Arctic, and Iowa) for the structural ensemble of the Aβ monomer. The four familial mutations are found to have distinct consequences for the monomer structure.