PEG−peptide conjugates

The remarkable diversity of the self-assembly behavior of PEG−peptides is reviewed, including self-assemblies formed by PEG−peptides with β-sheet and α-helical (coiled-coil) peptide sequences. The modes of self-assembly in solution and in the solid state are discussed. Additionally, applications in bionanotechnology and synthetic materials science are summarized.

Interactions between lipid-free apolipoprotein-AI and a lipopeptide incorporating the RGDS cell adhesion motif

The interaction of a designed bioactive lipopeptide C16-GGGRGDS, comprising a hexadecyl lipid chain attached to a functional heptapeptide, with the lipid-free apoliprotein, Apo-AI, is examined. This apolipoprotein is a major component of high density lipoprotein and it is involved in lipid metabolism and may serve as a biomarker for cardiovascular disease and Alzheimers’ disease. We find via isothermal titration calorimetry that binding between the lipopeptide and Apo-AI occurs up to a saturation condition, just above equimolar for a 10.7 μM concentration of Apo-AI. A similar value is obtained from circular dichroism spectroscopy, which probes the reduction in α-helical secondary structure of Apo-AI upon addition of C16-GGGRGDS. Electron microscopy images show a persistence of fibrillar structures due to self-assembly of C16-GGGRGDS in mixtures with Apo-AI above the saturation binding condition. A small fraction of spheroidal or possibly “nanodisc” structures was observed. Small-angle X-ray scattering (SAXS) data for Apo-AI can be fitted using a published crystal structure of the Apo-AI dimer. The SAXS data for the lipopeptide/ Apo-AI mixtures above the saturation binding conditions can be fitted to the contribution from fibrillar structures coexisting with flat discs corresponding to Apo-AI/lipopeptide aggregates.

Spontaneous local symmetry breaking: a conformational study of glycine on Cu{311}

Understanding the interplay between intrinsic molecular chirality and chirality of the bonding footprint is crucial in exploiting enantioselectivity at surfaces. As such, achiral glycine and chiral alanine are the most obvious candidates if one is to study this interplay on different surfaces. Here, we have investigated the adsorption of glycine on Cu{311} using reflection-absorption infrared spectroscopy, low-energy electron diffraction, temperature-programmed desorption and first-principles density-functional theory. This combination of techniques has allowed us to accurately identify the molecular conformations present under different conditions, and discuss the overlayer structure in the context of the possible bonding footprints. We have observed coverage-dependent local symmetry breaking, with three-point bonded glycinate moieties forming an achiral arrangement at low coverages, and chirality developing with the presence of two-point bonded moieties at high coverages. Comparison with previous work on the self-assembly of simple amino acids on Cu{311} and the structurally-similar Cu{110} surface has allowed us to rationalise the different conditions necessary for the formation of ordered chiral overlayers.

Repulsion-induced surface-migration, by ballistics and bounce

The motion of adsorbate molecules across surfaces is fundamental to self-assembly, material growth, and heterogeneous catalysis. Recent Scanning Tunneling Microscopy studies have demonstrated the electron-induced long-range surface-migration of ethylene, benzene, and related molecules, moving tens of Angstroms across Si(100). We present a model of the previously unexplained long-range recoil of chemisorbed ethylene across the surface of silicon. The molecular dynamics reveal two key elements for directed long-range migration: first ‘ballistic’ motion that causes the molecule to leave the ab initio slab of the surface traveling 3–8 Å above it out of range of its roughness, and thereafter skipping-stone ‘bounces’ that transport it further to the observed long distances. Using a previously tested Impulsive Two-State model, we predict comparable long-range recoil of atomic chlorine following electron-induced dissociation of chlorophenyl chemisorbed at Cu(110)

The role of protecting groups in the formation of organogels through a nano-fibrillar network formed by self-assembling terminally protected tripeptides

A series of eight synthetic self-assembling terminally blocked tripeptides have been studied for gelation. Some of them form gels in various aromatic solvents including benzene, toluene, xylene, and chlorobenzene. It has been found that the protecting groups play an important role in the formation of organogels. It has been observed that, if the C-terminal has been changed from methyl ester to ethyl ester the gelation property does not change significantly (keeping the N-terminal protecting group same), while the change of the protecting group from ethyl ester to isopropyl ester completely abolishes the gelation property. Similarly, keeping the identical C-terminal protecting group (methyl ester) the results of the gelation study indicate that the substitution of N-terminal protection Boc-(tert-butyloxycarbonyl) to Cbz-(benzyloxycarbonyl) does change the gelation property insignificantly, while the change from Boc- to pivaloyl (Piv-) or acetyl (Ac-) group completely eliminates the gelation property. Morphological studies of the dried gels of two of the peptides indicate the presence of an entangled nano-fibrillar network that might be responsible for gelation. FTIR studies of the gels demonstrate that an intermolecular hydrogen bonding network is formed during gelation. Results of X-ray powder diffraction studies for these gelator peptides in different states (dried gels, gel, and bulk solids) reflected that the structure in the wet gel is distinctly different from the dried gel and solid state structures. Single crystal X-ray diffraction studies of a non-gelator peptide, which is structurally similar to the gelator molecules reveal that the peptide forms an antiparallel beta-sheet structure in crystals. (c) 2007 Elsevier Ltd. All rights reserved.

New copper(I) cyanide networks: interpenetration, self-penetration and polymorphism

The structures Of four alkali-metal copper (I) cyanides, KCu2(CN)(3)(H2O)-H-.-II (I), K2Cu3(CN)(5) (II), CsCu3(CN)(4) (III) and KCu3(CN)(4) (IV) are described. Three of these, ((II)-(IV)), with previously unknown ACN:CuCN ratios have new copper-cyanide frameworks, whilst (1) is a new polymorph of KCu2(CN)(3)(H2O)-H-.. These structures are discussed in terms of assembly from the simple building units Cu(CN)(2/2), Cu(CN)(3/2), Cu(CN)(2/2)(CN)(1/1) and Cu(CN)(4/2). Compounds (I), (II) and (III) are layered materials based on (6,3) nets containing (CuCN)(6) rings (I) and (CuCN)(8) rings (II) and (III). In compound (IV), (4,4) nets containing (CuCN)(12) rings link to generate a three-dimensional network. Both (III) and (IV) are examples of interpenetrating solids in which two and four identical networks interweave, respectively. These materials illustrate the structural versatility of copper (I) in cyanide frameworks. (c) 2006 Elsevier SAS. All rights reserved.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Attention through self-synchronisation in the spiking neuron stochastic diffusion network

The paper discusses ensemble behaviour in the Spiking Neuron Stochastic Diffusion Network, SNSDN, a novel network exploring biologically plausible information processing based on higher order temporal coding. SNSDN was proposed as an alternative solution to the binding problem [1]. SNSDN operation resembles Stochastic Diffusin on Search, SDS, a non-deterministic search algorithm able to rapidly locate the best instantiation of a target pattern within a noisy search space ([3], [5]). In SNSDN, relevant information is encoded in the length of interspike intervals. Although every neuron operates in its own time, ‘attention’ to a pattern in the search space results in self-synchronised activity of a large population of neurons. When multiple patterns are present in the search space, ‘switching of at- tention’ results in a change of the synchronous activity. The qualitative effect of attention on the synchronicity of spiking behaviour in both time and frequency domain will be discussed.

A robust two-dimensional hydrogen-bonded network for the predictable assembly of ternary co-crystals of furosemide

Consideration of the geometrical features of the functional groups present in furosemide has enabled synthesis of a series of ternary co-crystals with predictable structural features, containing a robust asymmetric two-dimensional network.

Collaborative eLearning Assistant Network: caring agents are conscious agents

Would a research assistant - who can search for ideas related to those you are working on, network with others (but only share the things you have chosen to share), doesn’t need coffee and who might even, one day, appear to be conscious - help you get your work done? Would it help your students learn? There is a body of work showing that digital learning assistants can be a benefit to learners. It has been suggested that adaptive, caring, agents are more beneficial. Would a conscious agent be more caring, more adaptive, and better able to deal with changes in its learning partner’s life? Allow the system to try to dynamically model the user, so that it can make predictions about what is needed next, and how effective a particular intervention will be. Now, given that the system is essentially doing the same things as the user, why don’t we design the system so that it can try to model itself in the same way? This should mimic a primitive self-awareness. People develop their personalities, their identities, through interacting with others. It takes years for a human to develop a full sense of self. Nobody should expect a prototypical conscious computer system to be able to develop any faster than that. How can we provide a computer system with enough social contact to enable it to learn about itself and others? We can make it part of a network. Not just chatting with other computers about computer ‘stuff’, but involved in real human activity. Exposed to ‘raw meaning’ – the developing folksonomies coming out of the learning activities of humans, whether they are traditional students or lifelong learners (a term which should encompass everyone). Humans have complex psyches, comprised of multiple strands of identity which reflect as different roles in the communities of which they are part – so why not design our system the same way? With multiple internal modes of operation, each capable of being reflected onto the outside world in the form of roles – as a mentor, a research assistant, maybe even as a friend. But in order to be able to work with a human for long enough to be able to have a chance of developing the sort of rich behaviours we associate with people, the system needs to be able to function in a practical and helpful role. Unfortunately, it is unlikely to get a free ride from many people (other than its developer!) – so it needs to be able to perform a useful role, and do so securely, respecting the privacy of its partner. Can we create a system which learns to be more human whilst helping people learn?

Assembly and maintenance of the sarcomere night and day

The assembly of sarcomeric proteins into the highly organized structure of the sarcomere is an ordered and complex process involving an array of structural and associated proteins. The sarcomere has shown itself to be considerably more complex than ever envisaged and may be considered one of the most complex macromolecular assemblies in biology. Studies over the last decade have helped to put a new face on the sarcomere, and, as such, the sarcomere is being redefined as a dynamic network of proteins capable of generating force and signalling with other cellular compartments and metabolic enzymes capable of controlling many facets of striated myocyte biology.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

A novel self-healing supramolecular polymer system

Utilising supramolecular pi-pi stacking interactions to drive miscibility in two-component polymer blends offers a novel approach to producing materials with unique properties. We report in this paper the preparation of a supramolecular polymer network that exploits this principle. A low molecular weight polydiimide which contains multiple pi-electron-poor receptor sites along its backbone forms homogeneous films with a siloxane polymer that features pi-electron-rich pyrenyl end-groups. Compatibility results from a complexation process that involves chain-folding of the polydiimide to create an optimum binding site for the pi-electron-rich chain ends of the polysiloxane. These complementary pi-electron-rich and -poor receptors exhibit rapid and reversible complexation behaviour in solution, and healable characteristics in the solid state in response to temperature. A mechanism is proposed for this thermoreversible healing behaviour that involves disruption of the intermolecular pi-pi stacking cross-links as the temperature of the supramolecular film is increased. The low T-g siloxane component can then flow and as the temperature of the blend is decreased, pi-pi stacking interactions drive formation of a new network and so lead to good damage-recovery characteristics of the two-component blend.

Low molecular weight organogelators from self-assembling synthetic tripeptides with coded amino acids: morphological, structural, thermodynamic and spectroscopic investigations

A series of self-assembling terminally blocked tripeptides (containing coded amino acids) form gels in various aromatic solvents including benzene, toluene, xylenes at low concentrations. However these tripeptides do not form gels in aliphatic hydrocarbons like n-hexane, cyclohexane, n-decane etc. Morphological studies of the dried gel indicate the presence of an entangled fibrous network, which is responsible for gelation. Differential scanning calorimetric (DSC) studies of the gels produced by peptide 1 clearly demonstrates thermoreversible nature of the gel and tripeptide-solvent complex may be produced during gel formation. FT-IR and H-1 NMR studies of the gels demonstrate that an intermolecular hydrogen-bonding network is formed during gelation. Single crystal X-ray diffraction studies for peptides 1, 2 and 3 have been performed to investigate the molecular arrangement that might be responsible for forming the fibrous network of these self-assembling peptide gelators. It has been found that the morph responsible for gelation of peptides 1, 2 and 3 in benzene is somewhat different from that of its xerogel.