Identifying the coiled-coil triple helix structure of β-peptide nanofibers at atomic resolution

Peptide self-assembly represents a powerful bottom-up approach to the fabrication of new nanomaterials. β3-peptides are non-natural peptides composed entirely of β-amino acids, which have an extra methylene in the backbone and we reported the first fibers derived from the self-assembly of β3-peptides that adopt unique 14-helical structures. β3-peptide assemblies represent a class of stable nanomaterials that can be used to generate bio- and magneto-responsive materials with proteolytic stability. However, the three-dimensional structure of many of these materials remains unknown. In order to develop structure-based criteria for the design of new β3-peptide-based biomaterials with tailored function, we investigated the structure of a tri-β3-peptide nanoassembly by molecular dynamics simulations and X-ray fiber diffraction analysis. Diffraction data was collected from aligned fibrils formed by Ac-β3[LIA] in water and used to inform and validate the model structure. Models with threefold radial symmetry resulted in stable fibers with a triple-helical coiled-coil motif and measurable helical pitch and periodicity. The fiber models revealed a hydrophobic core and twist along the fiber axis arising from a maximization of contacts between hydrophobic groups of adjacent tripeptides on the solvent-exposed fiber surface. These atomic structures of macro-scale fibers derived from β3-peptide-based materials provide valuable insight into the effects of the geometric placement of the side-chains and the influence of solvent on the core fiber structure which is perpetuated in the superstructure morphology

Rational design of monolayers for improved water evaporation mitigation

Seven chemically designed monolayer compounds were synthesized and investigated with comparison to the properties and water evaporation suppression ability of 1-hexadecanol and 1-octadecanol. Increasing the molecular weight and polarity of the compound headgroup drastically altered the characteristics and performance of the monolayer at the air/water interface. Contrary to the common expectation the monolayer\u27s lifetime on the water surface decreased with increasing number of ethylene oxy moieties, thus optimal performance for water evaporation suppression was achieved when only one ethylene oxy moiety was used. Replacing the hydroxyl headgroup with a methyl group and with multiple ethylene oxy moieties resulted in a loss of suppression capability, while an additional hydroxyl group provided a molecule with limited performance against water evaporation. Theoretical molecular simulation demonstrated that for exceptional performance, a candidate needs to possess a high equilibrium spreading pressure, the ability to sustain a highly ordered monolayer with a stable isotherm curve, and low tilt angle over the full studied range of surface pressures by simultaneously maintaining H-bonding to the water surface and between the monolayer chains

Facet-dependent interactions of islet amyloid polypeptide with gold nanoparticles: implications for fibril formation and peptide-induced lipid membrane disruption

A comprehensive understanding of the mechanisms of interaction between proteins or peptides and nanomaterials is crucial for the development of nanomaterial-based diagnos-tics and therapeutics. In this work, we systematically explored the interactions between citrate-capped gold nanoparticles (AuNPs) and islet amyloid polypeptide (IAPP), a 37-amino acid peptide hormone co-secreted with insulin from the pancreatic islet. We uti-lized diffusion-ordered spectroscopy, isothermal titration calorimetry, localized surface plasmon resonance spectroscopy, gel electrophoresis, atomic force microscopy, transmis-sion electron microscopy (TEM), and molecular dynamics (MD) simulations to systemati-cally elucidate the underlying mechanism of the IAPP−AuNP interactions. Because of the presence of a metal-binding sequence motif in the hydrophilic peptide domain, IAPP strongly interacts with the Au surface in both the monomeric and fibrillar states. Circular dichroism showed that AuNPs triggered the IAPP conformational transition from random coil to ordered structures (α-helix and β-sheet), and TEM imaging suggested the accelera-tion of IAPP fibrillation in the presence of AuNPs. MD simulations revealed that the IAPP−AuNP interactions were initiated by the N-terminal domain (IAPP residues 1−19), which subsequently induced a facet-dependent conformational change in IAPP. On a Au(111) surface, IAPP was unfolded and adsorbed directly onto the Au surface, while for the Au(100) surface, it interacted predominantly with the citrate adlayer and retained some helical conformation. The observed affinity of AuNPs for IAPP was further applied to reduce the level of peptide-induced lipid membrane disruption

Facet-dependent interactions of islet amyloid polypeptide with gold nanoparticles: Implications for fibril formation and peptide-induced lipid membrane disruption

A comprehensive understanding of the mechanisms of interaction between proteins or peptides and nanomaterials is crucial for the development of nanomaterial-based diagnostics and therapeutics. In this work, we systematically explored the interactions between citrate-capped gold nanoparticles (AuNPs) and islet amyloid polypeptide (IAPP), a 37-amino acid peptide hormone co-secreted with insulin from the pancreatic islet. We utilized diffusion-ordered spectroscopy, isothermal titration calorimetry, localized surface plasmon resonance spectroscopy, gel electrophoresis, atomic force microscopy, transmission electron microscopy (TEM), and molecular dynamics (MD) simulations to systematically elucidate the underlying mechanism of the IAPP–AuNP interactions. Because of the presence of a metal-binding sequence motif in the hydrophilic peptide domain, IAPP strongly interacts with the Au surface in both the monomeric and fibrillar states. Circular dichroism showed that AuNPs triggered the IAPP conformational transition from random coil to ordered structures (α-helix and β-sheet), and TEM imaging suggested the acceleration of IAPP fibrillation in the presence of AuNPs. MD simulations revealed that the IAPP–AuNP interactions were initiated by the N-terminal domain (IAPP residues 1–19), which subsequently induced a facet-dependent conformational change in IAPP. On a Au(111) surface, IAPP was unfolded and adsorbed directly onto the Au surface, while for the Au(100) surface, it interacted predominantly with the citrate adlayer and retained some helical conformation. The observed affinity of AuNPs for IAPP was further applied to reduce the level of peptide-induced lipid membrane disruption

Bioelectromagnetics research within an Australian context: the Australian centre for electromagnetic bioeffects research (ACEBR)

Mobile phone subscriptions continue to increase across the world, with the electromagnetic fields (EMF) emitted by these devices, as well as by related technologies such as Wi-Fi and smart meters, now ubiquitous. This increase in use and consequent exposure to mobile communication (MC)-related EMF has led to concern about possible health effects that could arise from this exposure. Although much research has been conducted since the introduction of these technologies, uncertainty about the impact on health remains. The Australian Centre for Electromagnetic Bioeffects Research (ACEBR) is a National Health and Medical Research Council Centre of Research Excellence that is undertaking research addressing the most important aspects of the MC-EMF health debate, with a strong focus on mechanisms, neurodegenerative diseases, cancer, and exposure dosimetry. This research takes as its starting point the current scientific status quo, but also addresses the adequacy of the evidence for the status quo. Risk communication research complements the above, and aims to ensure that whatever is found, it is communicated effectively and appropriately. This paper provides a summary of this ACEBR research (both completed and ongoing), and discusses the rationale for conducting it in light of the prevailing science.Sarah P. Loughran ... Jim Manavis ... Robert Vink ... et al

Dimensionality of Carbon Nanomaterials Determines the Binding and Dynamics of Amyloidogenic Peptides: Multiscale Theoretical Simulations

Experimental studies have demonstrated that nanoparticles can affect the rate of protein self-assembly, possibly interfering with the development of protein misfolding diseases such as Alzheimer's, Parkinson's and prion disease caused by aggregation and fibril formation of amyloid-prone proteins. We employ classical molecular dynamics simulations and large-scale density functional theory calculations to investigate the effects of nanomaterials on the structure, dynamics and binding of an amyloidogenic peptide apoC-II(60-70). We show that the binding affinity of this peptide to carbonaceous nanomaterials such as C60, nanotubes and graphene decreases with increasing nanoparticle curvature. Strong binding is facilitated by the large contact area available for π-stacking between the aromatic residues of the peptide and the extended surfaces of graphene and the nanotube. The highly curved fullerene surface exhibits reduced efficiency for π-stacking but promotes increased peptide dynamics. We postulate that the increase in conformational dynamics of the amyloid peptide can be unfavorable for the formation of fibril competent structures. In contrast, extended fibril forming peptide conformations are promoted by the nanotube and graphene surfaces which can provide a template for fibril-growth

"Janus" cyclic peptides: a new approach to amyloid fibril inhibition?

Cyclic peptides are increasingly being shown as powerful inhibitors of fibril formation, and have the potential to be therapeutic agents for combating many debilitating amyloid-related diseases. One such example is a cyclic peptide derivative from the human apolipoprotein C-II, which has the ability to inhibit fibril formation by the fibrillogenic peptide apoC-II(60-70). Using classical molecular dynamics and electronic structure calculations, we were able to provide insight into the interaction between the amyloidogenic peptide apoC-II(60-70) and its cyclic derivative, cyc(60-70). Our results showed that cyc(60-70) induced increased flexibility in apoC-II(60-70), suggesting that one mechanism by which cyc(60-70) inhibits fibrillisation is by destabilising apoC-II(60-70) structure, rendering it incapable of adopting fibril favouring conformations. In contrast, cyc(60-70) shows less flexibility upon binding to apoC-II(60-70), which is predominantly mediated by hydrophobic interactions between the aromatic rings of the peptides. This effectively creates a cap around the fibril-forming region of apoC-II(60-70) and generates an outer hydrophilic shell that discourages further apoC-II(60-70) peptide self-association. We showed that apoC-II(60-70) exhibited stronger binding affinity for the hydrophobic face of cyc(60-70) and weakest binding affinity for the hydrophilic side. This suggests that cyc(60-70) can be an effective fibril inhibitor due to its amphipathic character, like that of the "Janus"-type particles. This property can be exploited in the design of specific inhibitors of amyloid fibril formation

Quantitative design rules for protein-resistant surface coatings using machine learning

Abstract Preventing biological contamination (biofouling) is key to successful development of novel surface and nanoparticle-based technologies in the manufacturing industry and biomedicine. Protein adsorption is a crucial mediator of the interactions at the bio – nano -materials interface but is not well understood. Although general, empirical rules have been developed to guide the design of protein-resistant surface coatings, they are still largely qualitative. Herein we demonstrate that this knowledge gap can be addressed by using machine learning approaches to extract quantitative relationships between the material surface chemistry and the protein adsorption characteristics. We illustrate how robust linear and non-linear models can be constructed to accurately predict the percentage of protein adsorbed onto these surfaces using lysozyme or fibrinogen as prototype common contaminants. Our computational models could recapitulate the adsorption of proteins on functionalised surfaces in a test set with an r 2 of 0.82 and standard error of prediction of 13%. Using the same data set that enabled the development of the Whitesides rules, we discovered an extension to the original rules. We describe a workflow that can be applied to large, consistently obtained data sets covering a broad range of surface functional groups and protein types