Peptide mediated formation of hierarchically organized solution and solid state polymer nanostructures

Biologically-inspired peptide sequences have been explored as auxiliaries to mediate self-assembly of synthetic macromolecules into hierarchically organized solution and solid state nanostructures. Peptide sequences inspired by the coiled coil motif and "switch" peptides, which can adopt both amphiphilic alpha-helical and beta-strand conformations, were conjugated to poly(ethylene glycol) (PEG). The solution and solid state self-assembly of these materials was investigated using a variety of spectroscopic, scattering and microscopic techniques. These experiments revealed that the folding and organization properties of the peptide sequences are retained upon conjugation of PEG and that they provide the driving force for the formation of the different nanoscale structures which were observed. The possibility of using defined peptide sequences to direct structure formation of synthetic polymers together with the potential of peptide sequences to induce a specific biological response offers interesting prospects for the development of novel self-assembled and biologically active materials.

A supramolecular polymer based on tweezer-type pi-pi stacking interactions: molecular design for healability and enhanced toughness

An elastomeric, healable, supramolecular polymer blend comprising a chain-folding polyimide and a telechelic polyurethane with pyrenyl end groups is compatibilized by aromatic pi-pi stacking between the pi-electron-deficient diimide groups and the pi-electron-rich pyrenyl units. This interpolymer interaction is the key to forming a tough, healable, elastomeric material. Variable-temperature FTIR analysis of the bulk material also conclusively demonstrates the presence of hydrogen bonding, which complements the pi-pi stacking interactions. Variable-temperature SAXS analysis shows that the healable polymeric blend has a nanophase-separated morphology and that the X-ray contrast between the two types of domain increases with increasing temperature, a feature that is repeatable over several heating and cooling cycles. A fractured sample of this material reproducibly regains more than 95% of the tensile modulus, 91% of the elongation to break, and 77% of the modulus of toughness of the pristine material.

Sterically controlled recognition of macromolecular sequence information by molecular tweezers

Sequence-specific binding is demonstrated between pyrene-based tweezer molecules and soluble, high molar mass copolyimides. The binding involves complementary pi - pi stacking interactions, polymer chain-folding, and hydrogen bonding and is extremely sensitive to the steric environment around the pyromellitimide binding-site. A detailed picture of the intermolecular interactions involved has been obtained through single-crystal X-ray studies of tweezer complexes with model diimides. Ring-current magnetic shielding of polyimide protons by the pyrene '' arms '' of the tweezer molecule induces large complexation shifts of the corresponding H-1 NMR resonances, enabling specific triplet sequences to be identified by their complexation shifts. Extended comonomer sequences (triplets of triplets in which the monomer residues differ only by the presence or absence of a methyl group) can be '' read '' by a mechanism which involves multiple binding of tweezer molecules to adjacent diimide residues within the copolymer chain. The adjacent-binding model for sequence recognition has been validated by two conceptually different sets of tweezer binding experiments. One approach compares sequence-recognition events for copolyimides having either restricted or unrestricted triple-triplet sequences, and the other makes use of copolymers containing both strongly binding and completely nonbinding diimide residues. In all cases the nature and relative proportions of triple-triplet sequences predicted by the adjacent-binding model are fully consistent with the observed H-1 NMR data.

Open and closed edges of graphene layers

Edge structures of thermally treated graphite have been studied by means of atomically resolved high-resolution TEM. The method for the determination of a monolayer or more than one layer graphene sheets is established. A series of tilting experiments proves that the zigzag and armchair edges are mostly closed between adjacent graphene layers, and the number of dangling bonds is therefore minimized. Surprisingly bilayer graphene often exhibits AA stacking and is very hard to distinguish from a single graphene layer. Open edge structures with carbon dangling bonds can be found only in a local area where the closed (folding) edge is partially broken.

Conformational modulation of sequence recognition in synthetic macromolecules

The different triplet sequences in high molecular weight aromatic copolyimides comprising pyromellitimide units ("I") flanked by either ether-ketone ("K") or ether-sulfone residues ("S") show different binding strengths for pyrene-based tweezer-molecules. Such molecules bind primarily to the diimide unit through complementary π-π-stacking and hydrogen bonding. However, as shown by the magnitudes of 1H NMR complexation shifts and tweezer-polymer binding constants, the triplet "SIS" binds tweezer-molecules more strongly than "KIS" which in turn bind such molecules more strongly than "KIK". Computational models for tweezer-polymer binding, together with single-crystal X-ray analyses of tweezer-complexes with macrocyclic ether-imides, reveal that the variations in binding strength between the different triplet sequences arise from the different conformational preferences of aromatic rings at diarylketone and diarylsulfone linkages. These preferences determine whether or not chain-folding and secondary π−π-stacking occurs between the arms of the tweezermolecule and the 4,4'-biphenylene units which flank the central diimide residue.

How well do coupled models replicate ocean energetics relevant to ENSO?

Accurate replication of the processes associated with the energetics of the tropical ocean is necessary if coupled GCMs are to simulate the physics of ENSO correctly, including the transfer of energy from the winds to the ocean thermocline and energy dissipation during the ENSO cycle. Here, we analyze ocean energetics in coupled GCMs in terms of two integral parameters describing net energy loss in the system using the approach recently proposed by Brown and Fedorov (J Clim 23:1563–1580, 2010a) and Fedorov (J Clim 20:1108–1117, 2007). These parameters are (1) the efficiency c of the conversion of wind power into the buoyancy power that controls the rate of change of the available potential energy (APE) in the ocean and (2) the e-folding rate a that characterizes the damping of APE by turbulent diffusion and other processes. Estimating these two parameters for coupled models reveals potential deficiencies (and large differences) in how state-of-the-art coupled GCMs reproduce the ocean energetics as compared to ocean-only models and data assimilating models. The majority of the coupled models we analyzed show a lower efficiency (values of c in the range of 10–50% versus 50–60% for ocean-only simulations or reanalysis) and a relatively strong energy damping (values of a-1 in the range 0.4–1 years versus 0.9–1.2 years). These differences in the model energetics appear to reflect differences in the simulated thermal structure of the tropical ocean, the structure of ocean equatorial currents, and deficiencies in the way coupled models simulate ENSO.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM-based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets.

High-strength, healable, supramolecular polymer nanocomposites

A supramolecular polymer blend, formed via π-π interactions between a π-electron rich pyrenyl endcapped oligomer and a chain-folding oligomer containing pairs of π-electron poor naphthalene-diimide (NDI) units, has been reinforced with cellulose nanocrystals (CNCs) to afford a healable nanocomposite material. Nanocomposites with varying weight percentage of CNCs (from 1.25 to 20.0 wt.%) within the healable supramolecular polymeric matrix have been prepared via solvent casting followed by compression molding, and their mechanical properties and healing behavior have been evaluated. It is found that homogeneously dispersed films can be formed with CNCs at less than 10 wt.%. Above 10 wt.% CNC heterogeneous nanocomposites were obtained. All the nanocomposites formed could be re-healed upon exposure to elevated temperatures although, for the homogeneous films, it was found that the healing rate was reduced with increasing CNC content. The best combination of healing efficiency and mechanical properties was obtained with the 7.5 wt.% CNC nanocomposite which exhibited a tensile modulus enhanced by as much as a factor of 20 over the matrix material alone and could be fully re-healed at 85 °C within 30 minutes. Thus it is demonstrated that supramolecular nanocomposites can afford greatly enhanced mechanical properties relative to the unreinforced polymer, while still allowing efficient thermal healing.

Large is fast, small is tight: determinants of speed and affinity in subunit capture by a periplasmic chaperone

The chaperone/usher pathway assembles surface virulence organelles of Gram-negative bacteria, consisting of fibers of linearly polymerized protein subunits. Fiber subunits are connected through 'donor strand complementation': each subunit completes the immunoglobulin (Ig)-like fold of the neighboring subunit by donating the seventh β-strand in trans. Whereas the folding of Ig domains is a fast first-order process, folding of Ig modules into the fiber conformation is a slow second-order process. Periplasmic chaperones separate this process in two parts by forming transient complexes with subunits. Interactions between chaperones and subunits are also based on the principle of donor strand complementation. In this study, we have performed mutagenesis of the binding motifs of the Caf1M chaperone and Caf1 capsular subunit from Yersinia pestis and analyzed the effect of the mutations on the structure, stability, and kinetics of Caf1M-Caf1 and Caf1-Caf1 interactions. The results suggest that a large hydrophobic effect combined with extensive main-chain hydrogen bonding enables Caf1M to rapidly bind an early folding intermediate of Caf1 and direct its partial folding. The switch from the Caf1M-Caf1 contact to the less hydrophobic, but considerably tighter and less dynamic Caf1-Caf1 contact occurs via the zip-out-zip-in donor strand exchange pathway with pocket 5 acting as the initiation site. Based on these findings, Caf1M was engineered to bind Caf1 faster, tighter, or both faster and tighter. To our knowledge, this is the first successful attempt to rationally design an assembly chaperone with improved chaperone function.

Conductance of amyloid β based peptide filaments: structure–function relations.

Controlling the morphology of self-assembled peptide nanostructures, particularly those based on amyloid peptides, has been the focus of intense research. In order to exploit these structures in electronic applications, further understanding of their electronic behavior is required. In this work, the role of peptide morphology in determining electronic conduction along self-assembled peptide nanofilament networks is demonstrated. The peptides used in this work were based on the sequence AAKLVFF, which is an extension of a core sequence from the amyloid b peptide. We show that the incorporation of a non-natural amino acid, 2-thienylalanine, instead of phenylalanine improves the obtained conductance with respect to that obtained for a similar structure based on the native sequence, which was not the case for the incorporation of 3-thienylalanine. Furthermore, we demonstrate that the morphology of the self-assembled structures, which can be controlled by the solvent used in the assembly process, strongly affects the conductance, with larger conduction obtained for a morphology of long, straight filaments. Our results demonstrate that, similar to natural systems, the assembly and folding of peptides could be of great importance for optimizing their function as components of electronic devices. Hence, sequence design and assembly conditions can be used to control the performance of peptide based structures in such electronic applications.

Radiative forcing due to aviation water vapour emissions

Three emissions inventories have been used with a fully Lagrangian trajectory model to calculate the stratospheric accumulation of water vapour emissions from aircraft, and the resulting radiative forcing. The annual and global mean radiative forcing due to present-day aviation water vapour emissions has been found to be 0.9 [0.3 to 1.4] mW m^2. This is around a factor of three smaller than the value given in recent assessments, and the upper bound is much lower than a recently suggested 20 mW m^2 upper bound. This forcing is sensitive to the vertical distribution of emissions, and, to a lesser extent, interannual variability in meteorology. Large differences in the vertical distribution of emissions within the inventories have been identified, which result in the choice of inventory being the largest source of differences in the calculation of the radiative forcing due to the emissions. Analysis of Northern Hemisphere trajectories demonstrates that the assumption of an e-folding time is not always appropriate for stratospheric emissions. A linear model is more representative for emissions that enter the stratosphere far above the tropopause.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Grid warehousing of molecular dynamics protein unfolding data

With the increasing awareness of protein folding disorders, the explosion of genomic information, and the need for efficient ways to predict protein structure, protein folding and unfolding has become a central issue in molecular sciences research. Molecular dynamics computer simulations are increasingly employed to understand the folding and unfolding of proteins. Running protein unfolding simulations is computationally expensive and finding ways to enhance performance is a grid issue on its own. However, more and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. This paper describes efforts to provide a grid-enabled data warehouse for protein unfolding data. We outline the challenge and present first results in the design and implementation of the data warehouse.

Realistic mean field forward predictions for the integration of co-registered EEG/fMRI

Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a "proof of principle" forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1. MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving "Balloon-Windkessel" hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1]. Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.

A Lagrangian model of air-mass photochemistry and mixing using a trajectory ensemble: the Cambridge Tropospheric Trajectory model of Chemistry And Transport (CiTTyCAT) version 4.2

A Lagrangian model of photochemistry and mixing is described (CiTTyCAT, stemming from the Cambridge Tropospheric Trajectory model of Chemistry And Transport), which is suitable for transport and chemistry studies throughout the troposphere. Over the last five years, the model has been developed in parallel at several different institutions and here those developments have been incorporated into one "community" model and documented for the first time. The key photochemical developments include a new scheme for biogenic volatile organic compounds and updated emissions schemes. The key physical development is to evolve composition following an ensemble of trajectories within neighbouring air-masses, including a simple scheme for mixing between them via an evolving "background profile", both within the boundary layer and free troposphere. The model runs along trajectories pre-calculated using winds and temperature from meteorological analyses. In addition, boundary layer height and precipitation rates, output from the analysis model, are interpolated to trajectory points and used as inputs to the mixing and wet deposition schemes. The model is most suitable in regimes when the effects of small-scale turbulent mixing are slow relative to advection by the resolved winds so that coherent air-masses form with distinct composition and strong gradients between them. Such air-masses can persist for many days while stretching, folding and thinning. Lagrangian models offer a useful framework for picking apart the processes of air-mass evolution over inter-continental distances, without being hindered by the numerical diffusion inherent to global Eulerian models. The model, including different box and trajectory modes, is described and some output for each of the modes is presented for evaluation. The model is available for download from a Subversion-controlled repository by contacting the corresponding authors.