Comparison of structure determination methods for intrinsically disordered amyloid-β peptides

Transition Metal Ion FRET-Based Probe to Study Cu(II)-Mediated Amyloid- Ligand Binding

Recent therapeutic strategies suggest that small peptides can act as aggregation inhibitors of monomeric amyloid-β (Αβ) by inducing structural rearrangements upon complexation. However, characterizing the binding events in such dynamic and transient noncovalent complexes, especially in the presence of natively occurring metal ions, remains a challenge. Here, we deploy a combined transition metal ion Förster resonance energy transfer (tmFRET) and native ion mobility-mass spectrometry (IM-MS) approach to characterize the structure of mass- and charge-selected Aβ complexes with Cu(II) ions (a quencher) and a potential aggregation inhibitor, a small neuropeptide named leucine enkephalin (LE). We show conformational changes of monomeric Αβ species upon Cu(II)-binding, indicating an uncoiled N-terminus and a close interaction between the C-terminus and the central hydrophobic region. Furthermore, we introduce LE labeled at the N-terminus with a metal-chelating agent, nitrilotriacetic acid (NTA). This allows us to employ tmFRET to probe the binding even in low-abundance and transient Aβ-inhibitor-metal ion complexes. Complementary intramolecular distance and global shape information from tmFRET and native IM-MS, respectively, confirmed Cu(II) displacement toward the N-terminus of Αβ, which discloses the binding region and the inhibitor's orientation.

A review: Exploring the metabolic and structural characterisation of beta pleated amyloid fibril in human tissue using Raman spectrometry and SAXS

Amyloidosis is a deleterious condition caused by abnormal amyloid fibril build-up in living tissues. To date, 42 proteins that are linked to amyloid fibrils have been discovered. Amyloid fibril structure variation can affect the severity, progression rate, or clinical symptoms of amyloidosis. Since amyloid fibril build-up is the primary pathological basis for various neurodegenerative illnesses, characterization of these deadly proteins, particularly utilising optical techniques have been a focus. Spectroscopy techniques provide significant non-invasive platforms for the investigation of the structure and conformation of amyloid fibrils, offering a wide spectrum of analyses ranging from nanometric to micrometric size scales. Even though this area of study has been intensively explored, there still remain aspects of amyloid fibrillization that are not fully known, a matter hindering progress in treating and curing amyloidosis. This review aims to provide recent updates and comprehensive information on optical techniques for metabolic and proteomic characterization of β-pleated amyloid fibrils found in human tissue with thorough literature analysis of publications. Raman spectroscopy and SAXS are well established experimental methods for study of structural properties of biomaterials. With suitable models, they offer extended information for valid proteomic analysis under physiologically relevant conditions. This review points to evidence that despite limitations, these techniques are able to provide for the necessary output and proteomics indication in order to extrapolate the aetiology of amyloid fibrils for reliable diagnostic purposes. Our metabolic database may also contribute to elucidating the nature and function of the amyloid proteome in development and clearance of amyloid diseases.

Training Neural Network Models Using Molecular Dynamics Simulation Results to Efficiently Predict Cyclic Hexapeptide Structural Ensembles

Cyclic peptides have emerged as a promising class of therapeutics. However, their de novo design remains challenging, and many cyclic peptide drugs are simply natural products or their derivatives. Most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. The ability to characterize cyclic peptide structural ensembles would greatly aid their rational design. In a previous pioneering study, our group demonstrated that using molecular dynamics results to train machine learning models can efficiently predict structural ensembles of cyclic pentapeptides. Using this method, which was termed StrEAMM (Structural Ensembles Achieved by Molecular Dynamics and Machine Learning), linear regression models were able to predict the structural ensembles for an independent test set with R² = 0.94 between the predicted populations for specific structures and the observed populations in molecular dynamics simulations for cyclic pentapeptides. An underlying assumption in these StrEAMM models is that cyclic peptide structural preferences are predominantly influenced by neighboring interactions, namely, interactions between (1,2) and (1,3) residues. Here we demonstrate that for larger cyclic peptides such as cyclic hexapeptides, linear regression models including only (1,2) and (1,3) interactions fail to produce satisfactory predictions (R² = 0.47); further inclusion of (1,4) interactions leads to moderate improvements (R² = 0.75). We show that when using convolutional neural networks and graph neural networks to incorporate complex nonlinear interaction patterns, we can achieve R² = 0.97 and R² = 0.91 for cyclic pentapeptides and hexapeptides, respectively.

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

Ensemble Generation for Linear and Cyclic Peptides Using a Reservoir Replica Exchange Molecular Dynamics Implementation in GROMACS

The profile of shapes presented by a cyclic peptide modulates its therapeutic efficacy and is represented by the ensemble of its sampled conformations. Although some algorithms excel at creating a diverse ensemble of cyclic peptide conformations, they seldom address the entropic contribution of flexible conformations and often have significant practical difficulty producing an ensemble with converged and reliable thermodynamic properties. In this study, an accelerated molecular dynamics (MD) method, namely, reservoir replica exchange MD (R-REMD or Res-REMD), was implemented in GROMACS ver. 4.6.7 and benchmarked on two small cyclic peptide model systems: a cyclized furin cleavage site of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (cyclo-(CGPRRARSG)) and oxytocin (disulfide-bonded CYIQNCPLG). Additionally, we also benchmarked Res-REMD on alanine dipeptide and Trpzip2 to demonstrate its validity and efficiency over REMD. For Trpzip2, Res-REMD coupled with an umbrella-sampling-derived reservoir generated similar folded fractions as regular REMD but on a much faster time scale. For cyclic peptides, Res-REMD appeared to be marginally faster than REMD in ensemble generation. Finally, Res-REMD was more effective in sampling rare events such as trans to cis peptide bond isomerization. We provide a GitHub page with the modified GROMACS source code for running Res-REMD at https://github.com/PlotkinLab/Reservoir-REMD.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Protein Dynamics to Define and Refine Disordered Protein Ensembles

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R₂ relaxation rates and Lipari-Szabo order parameters (S² values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

Configurational Entropy of Folded Proteins and Its Importance for Intrinsically Disordered Proteins

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction-the connection to configurational entropy-and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R _h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

Cementum protein 1-derived peptide (CEMP 1-p1) modulates hydroxyapatite crystal formation in vitro

A cementum protein 1-derived peptide (CEMP1-p1) consisting of 20 amino acids from the CEMP1's N-terminus region: MGTSSTDSQQAGHRRCSTSN, and its role on the mineralization process in a cell-free system, was characterized. CEMP1-p1's physicochemical properties, crystal formation, and hydroxyapatite (HA) nucleation assays were performed. Crystals induced by CEMP1-p1 were analyzed by scanning electron microscopy, Fourier-transform infrared spectroscopy-attenuated total reflectance (FTIR-ATR), X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM), and atomic force microscopy. The results indicate that CEMP1-p1 lacks secondary structure, forms nanospheres that organize into three-dimensional structures, possesses affinity to HA, and induces its nucleation. CEMP1-p1 promotes the formation of spherical structures composed by densely packed prism-like crystals, which revealed a Ca/P ratio of 1.56, corresponding to HA. FTIR-ATR showed predominant spectrum peaks that correspond and are characteristic of HA and octacalcium phosphate (OCP). Analysis by XRD indicates that the crystals show planes with a preferential crystalline orientation for HA and for OCP. HRTEM showed interplanar distances that correspond to crystalline planes of HA and OCP. Crystals are composed by superimposed lamellae, which exhibit epitaxial growth, and each layer of the crystals is structured by nanocrystals. This study reveals that CEMP1-p1 regulates HA crystal formation, somehow mimicking the in vivo process of mineralized tissues bioformation.

bHLH-PAS Proteins: Their Structure and Intrinsic Disorder

The basic helix–loop–helix/Per-ARNT-SIM (bHLH–PAS) proteins are a class of transcriptional regulators, commonly occurring in living organisms and highly conserved among vertebrates and invertebrates. These proteins exhibit a relatively well-conserved domain structure: the bHLH domain located at the N-terminus, followed by PAS-A and PAS-B domains. In contrast, their C-terminal fragments present significant variability in their primary structure and are unique for individual proteins. C-termini were shown to be responsible for the specific modulation of protein action. In this review, we present the current state of knowledge, based on NMR and X-ray analysis, concerning the structural properties of bHLH–PAS proteins. It is worth noting that all determined structures comprise only selected domains (bHLH and/or PAS). At the same time, substantial parts of proteins, comprising their long C-termini, have not been structurally characterized to date. Interestingly, these regions appear to be intrinsically disordered (IDRs) and are still a challenge to research. We aim to emphasize the significance of IDRs for the flexibility and function of bHLH–PAS proteins. Finally, we propose modern NMR methods for the structural characterization of the IDRs of bHLH–PAS proteins.

HIV and Alzheimer's disease: complex interactions of HIV-Tat with amyloid β peptide and Tau protein

In patients infected with the human immunodeficiency virus (HIV), the HIV-Tat protein may be continually produced despite adequate antiretroviral therapy. As the HIV-infected population is aging, it is becoming increasingly important to understand how HIV-Tat may interact with proteins such as amyloid β and Tau which accumulate in the aging brain and eventually result in Alzheimer's disease. In this review, we examine the in vivo data from HIV-infected patients and animal models and the in vitro experiments that show how protein complexes between HIV-Tat and amyloid β occur through novel protein-protein interactions and how HIV-Tat may influence the pathways for amyloid β production, degradation, phagocytosis, and transport. HIV-Tat may also induce Tau phosphorylation through a cascade of cellular processes that lead to the formation of neurofibrillary tangles, another hallmark of Alzheimer's disease. We also identify gaps in knowledge and future directions for research. Available evidence suggests that HIV-Tat may accelerate Alzheimer-like pathology in patients with HIV infection which cannot be impacted by current antiretroviral therapy.

The combined force field-sampling problem in simulations of disordered amyloid-β peptides

Molecular dynamics simulations of intrinsically disordered proteins (IDPs) can provide high resolution structural ensembles if the force field is accurate enough and if the simulation sufficiently samples the conformational space of the IDP with the correct weighting of sub-populations. Here, we investigate the combined force field-sampling problem by testing a standard force field as well as newer fixed charge force fields, the latter specifically motivated for better description of unfolded states and IDPs, and comparing them with a standard temperature replica exchange (TREx) protocol and a non-equilibrium Temperature Cool Walking (TCW) sampling algorithm. The force field and sampling combinations are used to characterize the structural ensembles of the amyloid-beta peptides Aβ42 and Aβ43, which both should be random coils as shown recently by experimental nuclear magnetic resonance (NMR) and 2D Förster resonance energy transfer (FRET) experiments. The results illustrate the key importance of the sampling algorithm: while the standard force field using TREx is in poor agreement with the NMR J-coupling and nuclear Overhauser effect and 2D FRET data, when using the TCW method, the standard and optimized protein-water force field combinations are in very good agreement with the same experimental data since the TCW sampling method produces qualitatively different ensembles than TREx. We also discuss the relative merit of the 2D FRET data when validating structural ensembles using the different force fields and sampling protocols investigated in this work for small IDPs such as the Aβ42 and Aβ43 peptides.

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

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

Structural Characterization of N-WASP Domain V Using MD Simulations with NMR and SAXS Data

Because of their large conformational heterogeneity, structural characterization of intrinsically disordered proteins (IDPs) is very challenging using classical experimental methods alone. In this study, we use NMR and small-angle x-ray scattering (SAXS) data with multiple molecular dynamics (MD) simulations to describe the conformational ensemble of the fully disordered verprolin homology domain of the neural Aldrich syndrome protein involved in the regulation of actin polymerization. First, we studied several back-calculation software of SAXS scattering intensity and optimized the adjustable parameters to accurately calculate the SAXS intensity from an atomic structure. We also identified the most appropriate force fields for MD simulations of this IDP. Then, we analyzed four conformational ensembles of neural Aldrich syndrome protein verprolin homology domain, two generated with the program flexible-meccano with or without NMR-derived information as input and two others generated by MD simulations with two different force fields. These four conformational ensembles were compared to available NMR and SAXS data for validation. We found that MD simulations with the AMBER-03w force field and the TIP4P/2005s water model are able to correctly describe the conformational ensemble of this 67-residue IDP at both local and global level.

Mechanical and thermodynamic properties of Aβ42, Aβ40, and α-synuclein fibrils: a coarse-grained method to complement experimental studies

We perform molecular dynamics simulation on several relevant biological fibrils associated with neurodegenerative diseases such as Aβ₄₀, Aβ₄₂, and α-synuclein systems to obtain a molecular understanding and interpretation of nanomechanical characterization experiments. The computational method is versatile and addresses a new subarea within the mechanical characterization of heterogeneous soft materials. We investigate both the elastic and thermodynamic properties of the biological fibrils in order to substantiate experimental nanomechanical characterization techniques that are quickly developing and reaching dynamic imaging with video rate capabilities. The computational method qualitatively reproduces results of experiments with biological fibrils, validating its use in extrapolation to macroscopic material properties. Our computational techniques can be used for the co-design of new experiments aiming to unveil nanomechanical properties of biological fibrils from a point of view of molecular understanding. Our approach allows a comparison of diverse elastic properties based on different deformations , i.e., tensile (Y _L), shear (S), and indentation (Y _T) deformation. From our analysis, we find a significant elastic anisotropy between axial and transverse directions (i.e., Y _T > Y _L) for all systems. Interestingly, our results indicate a higher mechanostability of Aβ₄₂ fibrils compared to Aβ₄₀, suggesting a significant correlation between mechanical stability and aggregation propensity (rate) in amyloid systems. That is, the higher the mechanical stability the faster the fibril formation. Finally, we find that α-synuclein fibrils are thermally less stable than β-amyloid fibrils. We anticipate that our molecular-level analysis of the mechanical response under different deformation conditions for the range of fibrils considered here will provide significant insights for the experimental observations.

Characterization of intrinsically disordered proteins and their dynamic complexes: From in vitro to cell-like environments

Over the last two decades, it has become increasingly clear that a large fraction of the human proteome is intrinsically disordered or contains disordered segments of significant length. These intrinsically disordered proteins (IDPs) play important regulatory roles throughout biology, underlining the importance of understanding their conformational behavior and interaction mechanisms at the molecular level. Here we review recent progress in the NMR characterization of the structure and dynamics of IDPs in various functional states and environments. We describe the complementarity of different NMR parameters for quantifying the conformational propensities of IDPs in their isolated and phosphorylated states, and we discuss the challenges associated with obtaining structural models of dynamic protein-protein complexes involving IDPs. In addition, we review recent progress in understanding the conformational behavior of IDPs in cell-like environments such as in the presence of crowding agents, in membrane-less organelles and in the complex environment of the human cell.

Significantly different contact patterns between Aβ40 and Aβ42 monomers involving the N-terminal region

Aβ42 peptide, with two additional residues at C-terminus, aggregates much faster than Aβ40. We performed equilibrium replica-exchange molecular dynamics simulations of their monomers using our residue-specific force field. Simulated ³ J_{HNH α} -coupling constants agree excellently with experimental data. Aβ40 and Aβ42 have very similar local conformational features, with considerable β-strand structures in the segments: A2-H6 (A), L17-A21 (B), A30-V36 (C) of both peptides and V39-I41 (D) of Aβ42. Both peptides have abundant A-B and B-C contacts, but Aβ40 has much more contacts between A and C than Aβ42, which may retard its aggregation. Only Aβ42 has considerable A-B-C-D topology. Decreased probability of A-C contact in Aβ42 relates to the competition from C-D contact. Increased A-C contact probability may also explain the slower aggregation of A2T and A2V mutants of Aβ42.

Looking at the Disordered Proteins through the Computational Microscope

Intrinsically disordered proteins (IDPs) have attracted wide interest over the past decade due to their surprising prevalence in the proteome and versatile roles in cell physiology and pathology. A large selection of IDPs has been identified as potential targets for therapeutic intervention. Characterizing the structure-function relationship of disordered proteins is therefore an essential but daunting task, as these proteins can adapt transient structure, necessitating a new paradigm for connecting structural disorder to function. Molecular simulation has emerged as a natural complement to experiments for atomic-level characterizations and mechanistic investigations of this intriguing class of proteins. The diverse range of length and time scales involved in IDP function requires performing simulations at multiple levels of resolution. In this Outlook, we focus on summarizing available simulation methods, along with a few interesting example applications. We also provide an outlook on how these simulation methods can be further improved in order to provide a more accurate description of IDP structure, binding, and assembly.

Influence of Solvent Representation on Nuclear Shielding Calculations of Protonation States of Small Biological Molecules

In this study, we assess the influence of solvation on the accuracy and reliability of isotropic nuclear magnetic shielding calculations for amino acids in comparison to experimental data. We focus particularly on the performance of solvation methods for different protonation states, as biological molecules occur almost exclusively in aqueous solution and are subject to protonation with pH. We identify significant shortcomings of current implicit solvent models and present a hybrid solvation approach that improves agreement with experimental data by taking into account the presence of direct interactions between amino acid protonation state and water molecules.

Effect of a Paramagnetic Spin Label on the Intrinsically Disordered Peptide Ensemble of Amyloid-β

Paramagnetic relaxation enhancement is an NMR technique that has yielded important insight into the structure of folded proteins, although the perturbation introduced by the large spin probe might be thought to diminish its usefulness when applied to characterizing the structural ensembles of intrinsically disordered proteins (IDPs). We compare the computationally generated structural ensembles of the IDP amyloid-β42 (Aβ42) to an alternative sequence in which a nitroxide spin label attached to cysteine has been introduced at its N-terminus. Based on this internally consistent computational comparison, we find that the spin label does not perturb the signature population of the β-hairpin formed by residues 16-21 and 29-36 that is dominant in the Aβ42 reference ensemble. However, the presence of the tag induces a strong population shift in a subset of the original Aβ42 structural sub-populations, including a sevenfold enhancement of the β-hairpin formed by residues 27-31 and 33-38. Through back-calculation of NMR observables from the computational structural ensembles, we show that the structural differences between the labeled and unlabeled peptide would be evident in local residual dipolar couplings, and possibly differences in homonuclear ¹H-¹H nuclear Overhauser effects (NOEs) and heteronuclear ¹H-¹⁵N NOEs if the paramagnetic contribution to the longitudinal relaxation does not suppress the NOE intensities in the real experiment. This work shows that molecular simulation provides a complementary approach to resolving the potential structural perturbations introduced by reporter tags that can aid in the interpretation of paramagnetic relaxation enhancement, double electron-electron resonance, and fluorescence resonance energy transfer experiments applied to IDPs.

Quarterly intrinsic disorder digest (April-May-June, 2014)

This is the 6^th issue of the Digested Disorder series that continues to use only 2 criteria for inclusion of a paper to this digest: The publication date (a paper should be published within the covered time frame) and the topic (a paper should be dedicated to any aspect of protein intrinsic disorder). The current digest issue covers papers published during the second quarter of 2014; i.e., during the period of April, May, and June of 2014. Similar to previous issues, the papers are grouped hierarchically by topics they cover, and for each of the included papers a short description is given on its major findings.

HIV Tat protein and amyloid-β peptide form multifibrillar structures that cause neurotoxicity

Deposition of amyloid-β plaques is increased in the brains of HIV-infected individuals, and the HIV transactivator of transcription (Tat) protein affects amyloidogenesis through several indirect mechanisms. Here, we investigated direct interactions between Tat and amyloid-β peptide. Our in vitro studies showed that in the presence of Tat, uniform amyloid fibrils become double twisted fibrils and further form populations of thick unstructured filaments and aggregates. Specifically, Tat binding to the exterior surfaces of the Aβ fibrils increases β-sheet formation and lateral aggregation into thick multifibrillar structures, thus producing fibers with increased rigidity and mechanical resistance. Furthermore, Tat and Aβ aggregates in complex synergistically induced neurotoxicity both in vitro and in animal models. Increased rigidity and mechanical resistance of the amyloid-β-Tat complexes coupled with stronger adhesion due to the presence of Tat in the fibrils may account for increased damage, potentially through pore formation in membranes.

Fibrillation-prone conformations of the amyloid-β-42 peptide at the gold/water interface

Proteins in the proximity of inorganic surfaces and nanoparticles may undergo profound adjustments that trigger biomedically relevant processes, such as protein fibrillation. The mechanisms that govern protein-surface interactions at the molecular level are still poorly understood. In this work, we investigate the adsorption onto a gold surface, in water, of an amyloid-β (Aβ) peptide, which is the amyloidogenic peptide involved in Alzheimer's disease. The entire adsorption process, from the peptide in bulk water to its conformational relaxation on the surface, is explored by large-scale atomistic molecular dynamics (MD) simulations. We start by providing a description of the conformational ensemble of Aβ in solution by a 22 μs temperature replica exchange MD simulation, which is consistent with previous results. Then, we obtain a statistical description of how the peptide approaches the gold surface by multiple MD simulations, identifying the preferential gold-binding sites and giving a kinetic picture of the association process. Finally, relaxation of the Aβ conformations at the gold/water interface is performed by a 19 μs Hamiltonian-temperature replica exchange MD simulation. We find that the conformational ensemble of Aβ is strongly perturbed by the presence of the surface. In particular, at the gold/water interface the population of the conformers akin to amyloid fibrils is significantly enriched, suggesting that this extended contact geometry may promote fibrillation.

Simulations of disordered proteins and systems with conformational heterogeneity

Intrinsically disordered proteins (IDPs) and protein regions can facilitate a wide variety of complex physiological processes such as binding, signaling, and formation of membraneless organelles. They can however also play pathological roles by aggregating into cytotoxic oligomers and fibrils. Characterizing the structure and function of disordered proteins is an onerous task, primarily because these proteins adopt transient structures, which are difficult to capture in experiments. Simulations have emerged as a powerful tool for interpreting and augmenting experimental measurements of IDPs. In this review we focus on computer simulations of disordered protein structures, functions, assemblies, and emerging questions that, taken together, give an overview of the field as it exists today.

Mechanism of Nucleation and Growth of Aβ40 Fibrils from All-Atom and Coarse-Grained Simulations

In this work, we characterize the nucleation and elongation mechanisms of the "diseased" polymorph of the amyloid-β 40 (Aβ40) fibril using an off-lattice coarse-grained (CG) protein model. After determining the nucleation size and subsequent stable protofibrillar structure from the CG model, validated with all-atom simulations, we consider the "lock and dock" and "activated monomer" fibril elongation mechanisms for the protofibril by statistical additions of a monomer drawn from four different ensembles of the free Aβ40 peptide to grow the fibril. Our CG model shows that the dominant mechanism for fibril elongation is the lock and dock mechanism across all monomer ensembles, even when the monomer is in the activated form. Although our CG model finds no thermodynamic difference between the two fibril elongation mechanisms, the activated monomer is found to be kinetically faster by a factor of 2 for the "locking" step compared with all other structured or unstructured monomer ensembles.

Comparing generalized ensemble methods for sampling of systems with many degrees of freedom

We compare two standard replica exchange methods using temperature and dielectric constant as the scaling variables for independent replicas against two new corresponding enhanced sampling methods based on non-equilibrium statistical cooling (temperature) or descreening (dielectric). We test the four methods on a rough 1D potential as well as for alanine dipeptide in water, for which their relatively small phase space allows for the ability to define quantitative convergence metrics. We show that both dielectric methods are inferior to the temperature enhanced sampling methods, and in turn show that temperature cool walking (TCW) systematically outperforms the standard temperature replica exchange (TREx) method. We extend our comparisons of the TCW and TREx methods to the 5 residue met-enkephalin peptide, in which we evaluate the Kullback-Leibler divergence metric to show that the rate of convergence between two independent trajectories is faster for TCW compared to TREx. Finally we apply the temperature methods to the 42 residue amyloid-β peptide in which we find non-negligible differences in the disordered ensemble using TCW compared to the standard TREx. All four methods have been made available as software through the OpenMM Omnia software consortium (http://www.omnia.md/).

Finding Our Way in the Dark Proteome

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However, approximately one-third of the human proteome is comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain "unseen" by traditional structural biology methods. This Perspective considers the challenges raised by the "Dark Proteome", in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high-throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a "computational beamline" that will allow key functional features to be identified in IDP structural ensembles.

Experimental Inferential Structure Determination of Ensembles for Intrinsically Disordered Proteins

We develop a Bayesian approach to determine the most probable structural ensemble model from candidate structures for intrinsically disordered proteins (IDPs) that takes full advantage of NMR chemical shifts and J-coupling data, their known errors and variances, and the quality of the theoretical back-calculation from structure to experimental observables. Our approach differs from previous formulations in the optimization of experimental and back-calculation nuisance parameters that are treated as random variables with known distributions, as opposed to structural or ensemble weight optimization or use of a reference ensemble. The resulting experimental inferential structure determination (EISD) method is size extensive with O(N) scaling, with N = number of structures, that allows for the rapid ranking of large ensemble data comprising tens of thousands of conformations. We apply the EISD approach on singular folded proteins and a corresponding set of ∼25 000 misfolded states to illustrate the problems that can arise using Boltzmann weighted priors. We then apply the EISD method to rank IDP ensembles most consistent with the NMR data and show that the primary error for ranking or creating good IDP ensembles resides in the poor back-calculation from structure to simulated experimental observable. We show that a reduction by a factor of 3 in the uncertainty of the back-calculation error can improve the discrimination among qualitatively different IDP ensembles for the amyloid-beta peptide.

Monomeric Aβ(1-40) and Aβ(1-42) Peptides in Solution Adopt Very Similar Ramachandran Map Distributions That Closely Resemble Random Coil

The pathogenesis of Alzheimer’s disease is characterized by the aggregation and fibrillation of amyloid peptides Aβ^1–40 and Aβ^1–42 into amyloid plaques. Despite strong potential therapeutic interest, the structural pathways associated with the conversion of monomeric Aβ peptides into oligomeric species remain largely unknown. In particular, the higher aggregation propensity and associated toxicity of Aβ^1–42 compared to that of Aβ^1–40 are poorly understood. To explore in detail the structural propensity of the monomeric Aβ^1–40 and Aβ^1–42 peptides in solution, we recorded a large set of nuclear magnetic resonance (NMR) parameters, including chemical shifts, nuclear Overhauser effects (NOEs), and J couplings. Systematic comparisons show that at neutral pH the Aβ^1–40 and Aβ^1–42 peptides populate almost indistinguishable coil-like conformations. Nuclear Overhauser effect spectra collected at very high resolution remove assignment ambiguities and show no long-range NOE contacts. Six sets of backbone J couplings (³J_HNHα, ³J_C′C′, ³J_C′Hα, ¹J_HαCα, ²J_NCα, and ¹J_NCα) recorded for Aβ^1–40 were used as input for the recently developed MERA Ramachandran map analysis, yielding residue-specific backbone ϕ/ψ torsion angle distributions that closely resemble random coil distributions, the absence of a significantly elevated propensity for β-conformations in the C-terminal region of the peptide, and a small but distinct propensity for α_L at K28. Our results suggest that the self-association of Aβ peptides into toxic oligomers is not driven by elevated propensities of the monomeric species to adopt β-strand-like conformations. Instead, the accelerated disappearance of Aβ NMR signals in D₂O over H₂O, particularly pronounced for Aβ^1–42, suggests that intermolecular interactions between the hydrophobic regions of the peptide dominate the aggregation process.

Accurate measurement of (3)J(HNHα) couplings in small or disordered proteins from WATERGATE-optimized TROSY spectra

Provided that care is taken in adjusting the WATERGATE element of a (1)H-(15)N TROSY-HSQC experiment, such that neither the water magnetization nor the (1)H(α) protons are inverted by its final 180° pulse, (3)JHNHα couplings can be measured directly from splittings in the (1)H dimension of the spectrum. With band-selective (1)H decoupling, very high (15)N resolution can be achieved. A complete set of (3)JHNHα values, ranging from 3.4 to 10.1 Hz was measured for the 56-residue third domain of IgG-binding protein G (GB3). Using the H-N-C(α)-H(α) dihedral angles extracted from a RDC-refined structure of GB3, (3)JHNHα values predicted by a previously parameterized Karplus equation agree to within a root-mean-square deviation (rmsd) of 0.37 Hz with the experimental data. Values measured for the Alzheimer's implicated Aβ(1-40) peptide fit to within an rmsd of 0.45 Hz to random coil (3)JHNHα values.

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

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

A facile method for expression and purification of (15)N isotope-labeled human Alzheimer's β-amyloid peptides from E. coli for NMR-based structural analysis

Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting millions of people worldwide. AD is characterized by the presence of extracellular plaques composed of aggregated/oligomerized β-amyloid peptides with Aβ42 peptide representing a major isoform in the senile plaques. Given the pathological significance of Aβ42 in the progression of AD, there is considerable interest in understanding the structural ensembles for soluble monomer and oligomeric forms of Aβ42. This report describes an efficient method to express and purify high quality (15)N isotope-labeled Aβ42 for structural studies by NMR. The protocol involves utilization of an auto induction system with (15)N isotope labeled medium, for high-level expression of Aβ42 as a fusion with IFABP. After the over-expression of the (15)N isotope-labeled IFABP-Aβ42 fusion protein in the inclusion bodies, pure (15)N isotope-labeled Aβ42 peptide is obtained following a purification method that is streamlined and improved from the method originally developed for the isolation of unlabeled Aβ42 peptide (Garai et al., 2009). We obtain a final yield of ∼ 6 mg/L culture for (15)N isotope-labeled Aβ42 peptide. Mass spectrometry and (1)H-(15)N HSQC spectra of monomeric Aβ42 peptide validate the uniform incorporation of the isotopic label. The method described here is equally applicable for the uniform isotope labeling with (15)N and (13)C in Aβ42 peptide as well as its other variants including any Aβ42 peptide mutants.

Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment

Intrinsically disordered proteins (IDPs) are notoriously challenging to study both experimentally and computationally. The structure of IDPs cannot be described by a single conformation but must instead be described as an ensemble of interconverting conformations. Atomistic simulations are increasingly used to obtain such IDP conformational ensembles. Here, we have compared the IDP ensembles generated by eight all-atom empirical force fields against primary small-angle X-ray scattering (SAXS) and NMR data. Ensembles obtained with different force fields exhibit marked differences in chain dimensions, hydrogen bonding, and secondary structure content. These differences are unexpectedly large: changing the force field is found to have a stronger effect on secondary structure content than changing the entire peptide sequence. The CHARMM 22* ensemble performs best in this force field comparison: it has the lowest error in chemical shifts and J-couplings and agrees well with the SAXS data. A high population of left-handed α-helix is present in the CHARMM 36 ensemble, which is inconsistent with measured scalar couplings. To eliminate inadequate sampling as a reason for differences between force fields, extensive simulations were carried out (0.964 ms in total); the remaining small sampling uncertainty is shown to be much smaller than the observed differences. Our findings highlight how IDPs, with their rugged energy landscapes, are highly sensitive test systems that are capable of revealing force field deficiencies and, therefore, contributing to force field development.

The Dependence of Amyloid-β Dynamics on Protein Force Fields and Water Models

We studied the dynamics of Aβ40 , involved in Alzheimer's disease, by using 21 methods combined from Amber03, Amber99sb-ILDN, Charmm27, Charmm22*, OPLS-2001, OPLS-2006, OPLS-2008, Gromos96-43a1, Gromos96-53a6, Gromos96-54a7, and the water models SPC, TIP3P, TIP4P. Major differences in the structural ensembles were systematized: Amber03, Charmm27, and Gromos96-54a7 stabilize the helices; Gromos96-43a1 and Gromos53a6 favor the β-strands (with Charmm22* and Amber99sb-ILDN in between), and OPLS produces unstructured ensembles. The accuracy of the NMR chemical shifts was in the order: Charmm22*>Amber99sb-ILDN>OPLS-2008≈Gromos96-43a1>Gromos96-54a7≈OPLS-2001>OPLS-2006>Gromos96-53a6>Charmm27>Amber03. The computed (3) JHNHα -coupling constants were sensitive to experiment type and Karplus parameterization. Overall, the ensembles of Charmm22* and Amber99sb-ILDN provided the best agreement with experimental NMR and circular dichroism data, providing a model for the real Aβ monomer ensemble. Also, the polar water model TIP3P significantly favored helix and compact conformations.

Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins

There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.

Disordered structural ensembles of vasopressin and oxytocin and their mutants

Vasopressin and oxytocin are intrinsically disordered cyclic nonapeptides belonging to a family of neurohypophysial hormones. Although unique in their functions, these peptides differ only by two residues and both feature a tocin ring formed by the disulfide bridge between first and sixth cysteine residues. This sequence and structural similarity are experimentally linked to oxytocin agonism at vasopressin receptors and vasopressin antagonism at oxytocin receptors. Yet single- or double-residue mutations in both peptides have been shown to have drastic impacts on their activities at either receptor, and possibly the ability to bind to their neurophysin carrier protein. In this study we perform molecular dynamics simulations of the unbound native and mutant sequences of the oxytocin and vasopressin hormones to characterize their structural ensembles. We classify the subpopulations of these structural ensembles on the basis of the distributions of radius of gyration and secondary structure and hydrogen-bonding features of the canonical tocin ring and disordered tail region. We then relate the structural changes observed in the unbound form of the different hormone sequences to experimental information about peptide receptor binding, and more indirectly, carrier protein binding affinity, receptor activity, and protease degradation. This study supports the hypothesis that the structural characteristics of the unbound form of an IDP can be used to predict structural or functional preferences of its functional bound form.