Ensemble Of Peptide Research Articles

A novel fluorescent and colorimetric dual-signals peptide-based probe for sequential recognition of Cu(II) and cimetidine and its multi-functional applications

A novel dual-signals peptide-based probe FDKSH was designed and synthesized based on 5-carboxy fluorescein (5-FAM) labelled tetrapeptide backbone (Asp-Lys-Ser-His-NH2). FDKSH exhibited a highly selective fluorescence “on-off” response and obvious colorimetric recognition to Cu2+ in 10 mM HEPES buffer solution at pH 7.4. Meanwhile, the limit of detection (LOD) was found to be 62.1 nM, which was much lower than that of stated by World Health Organization (WHO) in drinking water (30.0 μM). As a new promising cascade probe, the Cu(II)-assisted peptide ensemble probe (FDKSH-Cu2+ ensemble) has very high selectivity for cimetidine by the displacement method with LOD of 63.5 nM. In addition, FDKSH was successfully used to determine Cu2+ and cimetidine in two actual water samples. Additionally, FDKSH was also successfully used for monitoring Cu2+ and cimetidine in living cells and zebrafish, and showed low toxicity and excellent fluorescence imaging performance. It is worth noting that FDKSH-impregnated test strips exhibited significant fluorescence and colorimetric color changes for Cu2+ and cimetidine directly observed by naked eye. What’s more, smartphone with RGB color recognition function provided a new method for semi-quantitative visual detection of Cu2+ and cimetidine by analyzing different concentrations of Cu2+ and cimetidine without expensive instruments.

Digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational space of intrinsically disordered peptides.

We propose digital nets conformational sampling (DNCS) - an enhanced sampling technique to explore the conformational ensembles of peptides, especially intrinsically disordered peptides (IDPs). The DNCS algorithm relies on generating history-dependent samples of dihedral variables using bitwise XOR operations and binary angle measurements (BAM). The algorithm was initially studied using met-enkephalin, a highly elusive neuropeptide. The DNCS method predicted near-native structures and the energy landscape of met-enkephalin was observed to be in direct correlation with earlier studies on the neuropeptide. Clustering analysis revealed that there are only 24 low-lying conformations of the molecule. The DNCS method has then been tested for predicting optimal conformations of 42 oligopeptides of length varying from 3 to 8 residues. The closest-to-native structures of 86% of cases are near-native and 24% of them have a root mean square deviation of less than 1.00 Å with respect to their crystal structures. The results obtained reveal that the DNCS method performs well, that too in less computational time.

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

Visceral Leishmaniasis (VL) is a neglected tropical disease of public health importance in the Indian subcontinent. Despite consistent elimination initiatives, the disease has not yet been eliminated and there is an increased risk of resurgence from active VL reservoirs including asymptomatic, post kala azar dermatitis leishmaniasis (PKDL) and HIV-VL co-infected individuals. To achieve complete elimination and sustain it in the long term, a prophylactic vaccine, which can elicit long lasting immunity, is desirable. In this study, we employed immunoinformatic tools to design a multi-subunit epitope vaccine for the Indian population by targeting antigenic secretory proteins screened from the Leishmania donovani proteome. Out of 8014 proteins, 277 secretory proteins were screened for their cellular location and proteomic evidence. Through NCBI BlastP, unique fragments of the proteins were cropped, and their antigenicity was evaluated. B-cell, HTL and CTL epitopes as well as IFN-ɣ, IL-17, and IL-10 inducers were predicted, manually mapped to the fragments and common regions were tabulated forming a peptide ensemble. The ensemble was evaluated for Class I MHC immunogenicity and toxicity. Further, immunogenic peptides were randomly selected and used to design vaccine constructs. Eight vaccine constructs were generated by linking random peptides with GS linkers. Synthetic TLR-4 agonist, RS09 was used as an adjuvant and linked with the constructs using EAAK linkers. The predicted population coverage of the constructs was ∼99.8 % in the Indian as well as South Asian populations. The most antigenic, nontoxic, non-allergic construct was chosen for the prediction of secondary and tertiary structures. The 3D structures were refined and analyzed using Ramachandran plot and Z-scores. The construct was docked with TLR-4 receptor. Molecular dynamic simulation was performed to check for the stability of the docked complex. Comparative in silico immune simulation studies showed that the predicted construct elicited humoral and cell-mediated immunity in human host comparable to that elicited by Leish-F3, which is a promising vaccine candidate for human VL.

Accelerated cyclic peptide ensemble generation using reservoir REMD

Cyclic peptides have been widely used as potential therapeutics and scaffolded antigens due to their ability to bias the conformation to the desired structure using just a few amino acids. Although many tools have been developed to perform cyclic peptide structure prediction, there is little discussion on the ensemble generation of the cyclic peptides. We found that conventional molecular dynamics (MD) simulation is insufficient to sample the ensemble of small cyclic peptides (<10 amino acids), likely due to energy barriers rooted in the ring-like conformational constraint.

Probabilistic Modeling of RNA Ensembles Using NMR Chemical Shifts

NMR-derived chemical shifts are structural fingerprints that are sensitive to the underlying conformational distributions of molecules. Thus, chemical shift data are now routinely used to infer the dynamical or conformational ensembles of peptides and proteins. However, for RNAs, techniques for inferring their conformational ensembles from chemical shift data have received less attention. Here, we used chemical shift data and the Bayesian/maximum entropy (BME) approach to model the secondary structure ensembles of several single-stranded RNAs. Inspection of the resulting ensembles indicates that the secondary structure of the highest weighted (most probable) conformer in the ensemble typically resembled the known NMR structure. Furthermore, using apo chemical shifts measured for the HIV-1 TAR RNA, we found that our framework reproduces the expected structure yet predicts the existence of a previously unobserved base pair, which we speculate may be sampled transiently. We expect that the chemical shift-based BME (CS-BME) framework we describe here should find utility as a general strategy for modeling RNA ensembles using chemical shift data.

Representation of the conformational ensemble of peptides in coarse grained simulations.

In their native state, many proteins/peptides display an ensemble of conformations, rather than a unique tertiary structure. Novel experimental techniques have enabled a quantitative analysis of this structural heterogeneity. In molecular dynamics simulations, however, capturing this conformational ensemble quantitatively remains a major challenge even with all atom simulations. In coarse grained (CG) simulations, with fewer degrees of freedom, representation of the conformational ensemble becomes more problematic. Here, we revisit a CG model from our group, which was designed to address the conformational transferability problem by using the LKα14 peptide as a model system. The LKα14 peptide transitions from a random/unstructured state in dilute solution to a solely α-helical conformation upon aggregation as evidenced by circular dichroism. Here, we demonstrate that the structure/physics based approach, used in the original parameterization of our CG model, strongly depends on the reference system chosen and excluded volume interactions that are often considered to be of secondary importance. We first tune the excluded volume parameters by using both α-helix and β-sheet type structures as reference and then update the nonbonded interactions by using a goodness-of-fit metric for representation of the conformational ensemble of LKα14. We demonstrate that the updated model can recover the whole conformational ensemble quantitatively while maintaining the aggregation driven conformational transition. This balanced parametrization with regard to alternative secondary structures opens the door for the generalization of the CG model to other sequences, which we demonstrate on a β-sheet forming triblock peptide.

Investigating the Conformational Ensembles of Intrinsically Disordered Proteins with a Simple Physics-Based Model.

Intrinsically disordered proteins (IDPs) play an important role in an array of biological processes but present a number of fundamental challenges for computational modeling. Recently, simple polymer models have regained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single-chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. In particular, starting with a bead–spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics, and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins and solvent molecules.

Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck and, thereby, a low-dimensional embedding of the essential features. While variational autoencoders ensure continuity of the embedding by assuming a unimodal Gaussian prior, this is at odds with the multi-basin free-energy landscapes that typically arise from the identification of meaningful collective variables. In this work, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models, alanine dipeptide, and a challenging disordered peptide ensemble, demonstrating the enhanced clustering effect of the GMVAE prior compared to standard VAEs. The resulting embeddings appear to be promising representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics.

Investigating the Conformational Ensembles of Intrinsically Disordered Proteins with a Simple Physics-Based Model

The interactions of intrinsically disordered proteins (IDPs) play an important role in biological processes but present a number of fundamental challenges for computational modeling. While single chain conformational dynamics can be described by coarse-grained models with near-atomic resolution and specialized implicit solvent interactions, much simpler models are often adopted for investigating interactions between multiple IDPs. In fact, simple polymer models have regained popularity for interpreting the experimental characterization of IDPs. Homopolymer theory provides a strong foundation for understanding generic features of phenomena ranging from single chain conformational dynamics to the properties of entangled polymer melts, but is difficult to extend to the copolymer context. This challenge is magnified for proteins due to the variety of competing interactions and large deviations in side-chain properties. In this work, we apply a simple physics-based coarse-grained model for describing largely disordered conformational ensembles of peptides, based on the premise that sampling sterically-forbidden conformations can compromise the faithful description of both static and dynamical properties. The Hamiltonian of the employed model can be easily adjusted to investigate the impact of distinct interactions and sequence specificity on the randomness of the resulting conformational ensemble. We utilize this feature to explore various possible unbound ensembles for an IDP that undergoes coupled folding and binding. In particular, starting with a bead-spring-like model and then adding more detailed interactions one by one, we construct a hierarchical set of models and perform a detailed comparison of their properties. Our analysis clarifies the role of generic attractions, electrostatics and side-chain sterics, while providing a foundation for developing efficient models for IDPs that retain an accurate description of the hierarchy of conformational dynamics, which is nontrivially influenced by interactions with surrounding proteins.

Discovery of Tarantula Venom-Derived NaV1.7-Inhibitory JzTx-V Peptide 5-Br-Trp24 Analogue AM-6120 with Systemic Block of Histamine-Induced Pruritis.

Inhibitors of the voltage-gated sodium channel NaV1.7 are being investigated as pain therapeutics due to compelling human genetics. We previously identified NaV1.7-inhibitory peptides GpTx-1 and JzTx-V from tarantula venom screens. Potency and selectivity were modulated through attribute-based positional scans of native residues via chemical synthesis. Herein, we report JzTx-V lead optimization to identify a pharmacodynamically active peptide variant. Molecular docking of peptide ensembles from NMR into a homology model-derived NaV1.7 structure supported prioritization of key residues clustered on a hydrophobic face of the disulfide-rich folded peptide for derivatization. Replacing Trp24 with 5-Br-Trp24 identified lead peptides with activity in electrophysiology assays in engineered and neuronal cells. 5-Br-Trp24 containing peptide AM-6120 was characterized in X-ray crystallography and pharmacokinetic studies and blocked histamine-induced pruritis in mice after subcutaneous administration, demonstrating systemic NaV1.7-dependent pharmacodynamics. Our data suggests a need for high target coverage based on plasma exposure for impacting in vivo end points with selectivity-optimized peptidic NaV1.7 inhibitors.

Computational Investigation of the Effect of Backbone Chiral Inversions on Polypeptide Structure.

Studying a set of helix-folding polyalanine peptides with systematically inserted chiral inversions in explicit water, we investigate quantitatively the effect of chiral perturbations on the structural ensembles of the peptides, thereby assessing the extent to which the backbone structure is able to fold in the presence of systematic heterochiral perturbations. Starting from the homochiral l-Ala20 peptide, we invert the backbone chiralities of Ala residues one by one along a specific perturbation pathway, until reaching the homochiral d-Ala20 peptide. Analysis of the helical contents of the simulated structural ensembles of the peptides shows that even a single inversion in the middle of the peptide completely breaks the helical structure in its vicinity and drastically reduces the helical content of the peptide. Further inversions in the middle of the peptide monotonically decrease the original helical content, that is, the right-handed helical content for l-Ala, and increase the helical content of the opposite chirality. Further analysis of the peptide ensembles using several size- and shape-related order parameters also indicate the drastic global changes in the peptide structure due to the local effects caused by the chiral inversions, such as formation of a reverse turn. However, the degree of the structural changes introduced by opposite chirality substitutions depends on the position of the inversion.

Mapping the interface of alpha globin and eNOS: implications for increasing endogenous NO therapeutically

Nitric oxide signaling is a powerful mechanism for the regulation of blood pressure homeostasis, inducing vasodilation and reducing peripheral resistance. As such, disruption of NO‐inhibitory protein interactions may prove to be a novel therapeutic for the treatment of hypertensive disease states. Focusing on the recent discovery of the alpha globin/eNOS complex in vascular endothelium, we have designed an alpha globin mimetic peptide that binds to eNOS and increases NO signaling and dilation. A key step in the development of therapeutic agents targeting this complex is a high‐resolution structural model of the binding interface of alpha globin and eNOS. Thus, we have used solution NMR to determine the relevant structural details of the alpha globin mimetic peptide known to bind to eNOS. Interestingly, this twenty residue peptide has a stable population of both proline cis and trans isomers, potentially influencing the affinity of the peptide/eNOS interaction. Crosslinking mass spectrometry allows for the mapping of interacting residues between the peptide and eNOS after enzymatic digestion. Using cis and trans proline peptide ensembles, we have generated models of the peptide/eNOS complex through docking simulations which can be verified with full‐length alpha globin. The different conformations adopted by the peptide allows for potential new interaction modes that may not be possible with full alpha globin protein, thereby possibly increasing peptide/protein binding affinity. Determining the binding interface of the alpha globin/eNOS complex allows for targeted searches for molecular inhibitors of this alpha globin/eNOS interaction, in efforts for novel therapeutics to combat vasoconstrictive disease.Support or Funding InformationAmerican Heart Association Grant 16PRE31180040 (TCSK)NIH 088554 (BEI)NIH 087828 (LC)This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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.

Prediction and in vitro verification of potential CTL epitopes conserved among PRRSV-2 strains

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is the causative agent of one of the most important porcine diseases with a high impact on animal health, welfare, and production economy. PRRSV exhibits a multitude of immunoevasive strategies that, in combination with a very high mutation rate, has hampered the development of safe and broadly protective vaccines. Aiming at a vaccine inducing an effective cytotoxic T cell response, a bioinformatics approach was taken to identify conserved PRRSV-derived peptides predicted to react broadly with common swine leukocyte antigen (SLA) class I alleles. Briefly, all possible 9- and 10-mer peptides were generated from 104 complete PRRSV type 2 genomes of confirmed high quality, and peptides with high binding affinity to five common SLAs were identified combining the NetMHCpan and positional scanning combinatorial peptide libraries binding predictions. Predicted binders were prioritized according to genomic conservation and SLA coverage using the PopCover algorithm. From this, 53 peptides were acquired for further analysis. Binding affinity and stability of a subset of 101 peptide-SLA combinations were validated in vitro for 4 of the 5 SLAs. Eventually, 23% of the predicted peptide-SLA combinations showed to form complexes with a dissociation half-life ≥30 min. Additionally, combining the two prediction methods proved to be more robust across alleles than either method used alone in terms of predicted-to-observed correlations. In summary, our approach represents a finely tuned epitope prediction pipeline providing a rationally selected ensemble of peptides for future in vivo experiments with pigs expressing the included SLAs.

Structural Stability Among Hybrid Antimicrobial Peptide Cecropin A(1–8)–Magainin 2(1–12) and Its Analogues: A Computational Approach

Cecropin A–Magainin 2 (CA–MA) hybrid antimicrobial peptide (AMP), a combination of two naturally occurring AMPs, cecropin A and magainin 2 is preferred widely in biotechnological, nano and pharmaceutical applications. It exhibits a strong antibacterial activity with a characteristic reduced cytotoxic effect towards mammalian cells. In this study, three AMP structures native CA–MA hybrid and its tryptophan substitutes CA–MA L2 and CA–MA A2 was computationally studied to analyze their structural stability and functionality. Computational analysis like, intra-molecular interactions (25), relative stability (3.22) and instability index (−14.28) showed an increase in structural stability of native CA–MA hybrid. Additionally, the generated peptide ensembles showed a RMSD (3.98 A), RMSF (0.202 A), radius of gyration (11.98 A), ovality (3.33) and hydrophobicity (69.7%) supporting native CA–MA along with hydrogen bond strength (−4.212 kcal/mol) and distribution comparatively. The distribution of secondary structure in native CA–MA hybrid showed the sequential maintenance of stable helical content along with helical stability (52.25%) and computed free energy (−1.74 kcal/mol) in membrane mimicking environment proving its functional activity comparatively. This study aids in designing stable AMP biodrugs with low cytotoxicity in future, the result can be potentially extended to other AMPs to assist in their exploitation as peptide and nano drugs.

Refining Disordered Peptide Ensembles with Computational Amide I Spectroscopy: Application to Elastin-Like Peptides.

The characterization of intrinsically disordered protein (IDP) ensembles is complicated both by inherent heterogeneity and by the fact that many common experimental techniques function poorly when applied to IDPs. For this reason, the development of alternative structural tools for probing IDP ensembles has attracted considerable attention. Here we describe our recent work in developing experimental and computational tools for characterizing IDP ensembles using Amide I (backbone carbonyl stretch) vibrational spectroscopy. In this approach, the infrared (IR) absorption frequencies of isotope-labeled amide bonds probe their local electrostatic environments and structures. Empirical frequency maps allow us to use this spectroscopic data as a direct experimental test of atomistic structural models. We apply these methods to a family of short elastin-like peptides (ELPs), fragments of the elastin protein based around the Pro-Gly turn motif characteristic of the elastomeric segments of the full protein. Using a maximum entropy analysis that applies constraints from experimental spectra to weighting predicted spectra from molecular dynamics (MD) ensembles, we find that peptides with Ala or Val side chains preceding the Pro-Gly turn unit exhibit a stronger tendency toward extended structures than do Gly-Pro-Gly motifs, suggesting an important role for steric interactions in tuning the molecular properties of elastin.

Covalent perturbation as a tool for validation of identifications and PTM mapping applied to bovine alpha-crystallin.

Proteomic identifications hinge on the measurement of both parent and fragment masses and matching these to amino acid sequences via database search engines. The correctness of the identifications is assessed by statistical means. Here we present an experimental approach to test identifications. Chemical modification of all peptides in a sample leads to shifts in masses depending on the chemical properties of each peptide. The identification of a native peptide sequence and its perturbed version with a different parent mass and fragment ion masses provides valuable information. Labeling all peptides using reductive alkylation with formaldehyde is one such perturbation where the ensemble of peptides shifts mass depending on the number of reactive amine groups. Matching covalently perturbed fragmentation patterns from the same underlying peptide sequence increases confidence in the assignments and can salvage low scoring post-translationally modified peptides. Applying this strategy to bovine alpha-crystallin, we identify 9 lysine acetylation sites, 4 O-GlcNAc sites and 13 phosphorylation sites.

Thioflavin T templates amyloid β(1–40) conformation and aggregation pathway

Aβ(1–40) peptide supramolecular assembly and fibril formation processes are widely recognized to have direct implications in the progression of Alzheimer's disease. The molecular basis of this biological process is still unknown and there is a strong need of developing effective strategies to control the occurring events. To this purpose the exploitation of small molecules interacting with Aβ aggregation represents one of the possible routes. Moreover, the use specific labeling has represented so far one of the most common and effective methods to investigate such a process. This possibility in turn rests on the reliability of the probe/labels involved.Here we present evidences of the effect of Thioflavin T (ThT), a worldwide used fluorescent dye to monitor amyloid growth, on the Aβ(1–40) conformation, stability and aggregation. By combining experimental information and Molecular Dynamics simulation results, we show that the presence of ThT in solution affects peptide conformation inducing peculiar supramolecular association. In particular ThT interactions with specific Aβ(1–40) residues promote a rigid partially-folded conformation which shifts the balance between different species in solution toward a more aggregation-prone ensemble of peptides, leading to aggregation. Our findings suggest ways for developing strategies to reverse and block aggregation or to stimulate supramolecular assembly and consequently reduce the presence of transient oligomers. This investigation underlines the need of developing label-free techniques for unbiased quantitative studies of Aβ(1–40) aggregation processes.

Bottom-Up Coarse-Graining of Peptide Ensembles and Helix-Coil Transitions.

This work investigates the capability of bottom-up methods for parametrizing minimal coarse-grained (CG) models of disordered and helical peptides. We consider four high-resolution peptide ensembles that demonstrate varying degrees of complexity. For each high-resolution ensemble, we parametrize a CG model via the multiscale coarse-graining (MS-CG) method, which employs a generalized Yvon-Born-Green (g-YBG) relation to determine potentials directly (i.e., without iteration) from the high-resolution ensemble. The MS-CG method accurately describes high-resolution models that fluctuate about a single conformation. However, given the minimal resolution and simple molecular mechanics potential, the MS-CG method provides a less accurate description for a high-resolution peptide model that samples a disordered ensemble with multiple distinct conformations. We employ an iterative g-YBG method to develop a CG model that more accurately describes the relevant distribution functions and free energy surfaces for this disordered ensemble. Nevertheless, this more accurate model does not reproduce the cooperative helix-coil transition that is sampled by the high resolution model. By comparing the different models, we demonstrate that the errors in the MS-CG model primarily stem from the lack of cooperative interactions afforded by the minimal representation and molecular mechanics potential. This work demonstrates the potential of the MS-CG method for accurately modeling complex biomolecular structures, but also highlights the importance of more complex potentials for modeling cooperative transitions with a minimal CG representation.

Exploring the role of hydration and confinement in the aggregation of amyloidogenic peptides Aβ(16-22) and Sup35(7-13) in AOT reverse micelles.

Knowledge of how intermolecular interactions of amyloid-forming proteins cause protein aggregation and how those interactions are affected by sequence and solution conditions is essential to our understanding of the onset of many degenerative diseases. Of particular interest is the aggregation of the amyloid-β (Aβ) peptide, linked to Alzheimer's disease, and the aggregation of the Sup35 yeast prion peptide, which resembles the mammalian prion protein linked to spongiform encephalopathies. To facilitate the study of these important peptides, experimentalists have identified small peptide congeners of the full-length proteins that exhibit amyloidogenic behavior, including the KLVFFAE sub-sequence, Aβ16-22, and the GNNQQNY subsequence, Sup357-13. In this study, molecular dynamics simulations were used to examine these peptide fragments encapsulated in reverse micelles (RMs) in order to identify the fundamental principles that govern how sequence and solution environment influence peptide aggregation. Aβ16-22 and Sup357-13 are observed to organize into anti-parallel and parallel β-sheet arrangements. Confinement in the sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelles is shown to stabilize extended peptide conformations and enhance peptide aggregation. Substantial fluctuations in the reverse micelle shape are observed, in agreement with earlier studies. Shape fluctuations are found to facilitate peptide solvation through interactions between the peptide and AOT surfactant, including direct interaction between non-polar peptide residues and the aliphatic surfactant tails. Computed amide I IR spectra are compared with experimental spectra and found to reflect changes in the peptide structures induced by confinement in the RM environment. Furthermore, examination of the rotational anisotropy decay of water in the RM demonstrates that the water dynamics are sensitive to the presence of peptide as well as the peptide sequence. Overall, our results demonstrate that the RM is a complex confining environment where substantial direct interaction between the surfactant and peptides plays an important role in determining the resulting ensemble of peptide conformations. By extension the results suggest that similarly complex sequence-dependent interactions may determine conformational ensembles of amyloid-forming peptides in a cellular environment.