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.

Self-assembly of a peptide amphiphile containing l-carnosine and its mixtures with a multilamellar vesicle forming lipid

The self-assembly of the peptide amphiphile (PA) hexadecyl-(β-alaninehistidine) is examined in aqueous solution, along with its mixtures with multilamellar vesicles formed by DPPC (dipalmitoyl phosphatidylcholine). This PA, denoted C16-βAH, contains a dipeptide headgroup corresponding to the bioactive molecule L-carnosine. It is found to selfassemble into nanotapes based on stacked layers of molecules. Bilayers are found to coexist with monolayers in which the PA molecules pack with alternating up−down arrangement so that the headgroups decorate both surfaces. The bilayers become dehydrated as PA concentration increases and the number of layers in the stack decreases to produce ultrathin nanotapes comprised of 2−3 bilayers. Addition of the PA to DPPC multilamellar vesicles leads to a transition to well-defined unilamellar vesicles. The unique ability to modulate the stacking of this PA as a function of concentration, combined with its ability to induce a multilamellar to unilamellar thinning of DPPC vesicles, may be useful in biomaterials applications where the presentation of the peptide function at the surface of self-assembled nanostructures is crucial.

Influence of a non-ionic amphiphilic copolymer on the self-assembly of a peptide amphiphile that forms nanotapes

The influence of a non-ionic polymeric surfactant on the self-assembly of a peptide amphiphile (PA) that forms nanotapes is investigated using a combination of microscopic, scattering and spectroscopic techniques. Mixtures of Pluronic copolymer P123 with the PA C16-KTTKS in aqueous solution were studied at a fixed concentration of the PA at which it is known to self-assemble into extended nanotapes, but varying P123 concentration. We find that P123 can disrupt the formation of C16- KTTKS nanotapes, leading instead to cylindrical nanofibril structures. The spherical micelles formed by P123 at room temperature are disrupted in the presence of the PA. There is a loss of cloudiness in the solutions as the large nanotape aggregates formed by C16-KTTKS are broken up, by P123 solubilization. At least locally, b-sheet structure is retained, as confirmed by XRD and FTIR spectroscopy, even for solutions containing 20 wt% P123. This indicates, unexpectedly, that peptide secondary structure can be retained in solutions with high concentration of non-ionic surfactant. Selfassembly in this system exhibits slow kinetics towards equilibrium, the initial self-assembly being dependent on the order of mixing. Heating above the lipid chain melting temperature assists in disrupting trapped non-equilibrium states.

Self-assembly studies of a chiral bisurea-based superhydrogelator

A chiral bisurea-based superhydrogelator that is capable of forming supramolecular hydrogels at concentrations as low as 0.2 mm is reported. This soft material has been characterized by thermal studies, rheology, X-ray diffraction analysis, transmission electron microscopy (TEM), and by various spectroscopic techniques (electronic and vibrational circular dichroism and by FTIR and Raman spectroscopy). The expression of chirality on the molecular and supramolecular levels has been studied and a clear amplification of its chirality into the achiral analogue has been observed. Furthermore, thermal analysis showed that the hydroACHTUNGTRENUNGgel- ACHTUNGTRENUNGation of compound 1 has a high response to temperature, which corresponds to an enthalpy-driven self-assembly process. These particular thermal characteristics make these materials easy to handle for soft-application technologies

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

J-PMCRI: a methodology for inducing pre-pruned modular classification rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.

Induction of modular classification rules: using Jmax-pruning

The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pruning methods. Pruning methods use various metrics to truncate decision trees or to eliminate whole rules or single rule terms from a Prism rule set. For decision trees many pre-pruning and postpruning methods exist, however for Prism algorithms only one pre-pruning method has been developed, J-pruning. Recent work with Prism algorithms examined J-pruning in the context of very large datasets and found that the current method does not use its full potential. This paper revisits the J-pruning method for the Prism family of algorithms and develops a new pruning method Jmax-pruning, discusses it in theoretical terms and evaluates it empirically.

Jmax-pruning: a facility for the information theoretic pruning of modular classification rules

The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches pre-pruning facilities have been developed in order to prevent the induced classifiers from overfitting on noisy datasets, by cutting rule terms or whole rules or by truncating decision trees according to certain metrics. There have been many pre-pruning mechanisms developed for the TDIDT approach, but for the Prism family the only existing pre-pruning facility is J-pruning. J-pruning not only works on Prism algorithms but also on TDIDT. Although it has been shown that J-pruning produces good results, this work points out that J-pruning does not use its full potential. The original J-pruning facility is examined and the use of a new pre-pruning facility, called Jmax-pruning, is proposed and evaluated empirically. A possible pre-pruning facility for TDIDT based on Jmax-pruning is also discussed.

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

In order to gain knowledge from large databases, scalable data mining technologies are needed. Data are captured on a large scale and thus databases are increasing at a fast pace. This leads to the utilisation of parallel computing technologies in order to cope with large amounts of data. In the area of classification rule induction, parallelisation of classification rules has focused on the divide and conquer approach, also known as the Top Down Induction of Decision Trees (TDIDT). An alternative approach to classification rule induction is separate and conquer which has only recently been in the focus of parallelisation. This work introduces and evaluates empirically a framework for the parallel induction of classification rules, generated by members of the Prism family of algorithms. All members of the Prism family of algorithms follow the separate and conquer approach.

Design and self-assembly of a leucine-enkephalin analogue in different nanostructures: application of nanovesicles

An opioid (leucine-enkephalin) conformational analogue forms diverse nanostructures such as vesicles, tubes, and organogels through self-assembly. The nanovesicles encapsulate the natural hydrophobic drug curcumin and allow the controlled release through cation-generated porogens in membrane mimetic solvent.

Nano structures through self-assembly of protected hydrophobic amino acids: encapsulation of rhodamine B dye by proline-based nanovesicles

The development of novel molecules for the creation of nanometer structures with specific properties has been the current interest of this research. We have developed a set of molecules from hydrophobic omega- and alpha-amino acids by protecting the -NH(2) with Boc (t-butyloxycarbonyl) group and -CO(2)H with para-nitroanilide such as BocHN-Xx-CONH-(p-NO(2))center dot C(6)H(4), where Xx is gamma-aminobutyric acid (gamma-Abu), (L)-isoleucine, alpha-aminoisobutyric acid, proline, etc. These molecules generate various nanometer structures, such as nanofibrils, nanotubes and nanovesicles, in methanol/water through the self-assembly of bilayers in which the nitro benzene moieties are stacked in the middle and the Boc-protected amino acids parts are packed in the outer surface. The bilayers can be further stacked one over the other through hydrophobic interactions to form multilayer structure, which helps to generate different kinds of nanoscopic structures. The formation of the nanostructures has been facilitated through the participation of various noncovalent interactions, such as hydrophobic interactions, hydrogen bonding and aromatic p-stacking interactions. Fluorescence microscopy and UV studies reveal that the nanovesicles generated from pro-based molecule can encapsulate dye molecules which can be released by addition of acid (at pH 2). These single amino acid based molecules are both easy to synthesize and cost-effective and therefore offer novel scaffolds for the future design of nanoscale structures.

Formation of vesicles through solvent assisted self-assembly of hydrophobic pentapeptides: encapsulation and pH responsive release of dyes by the vesicles

In the biomimetic design two hydrophobic pentapetides Boc-Ile-Aib-Leu-Phe-Ala-OMe ( I) and Boc-Gly-Ile-Aib-Leu-Phe-OMe (II) (Aib: alpha-aminoisobutyric acid) containing one Aib each are found to undergo solvent assisted self-assembly in methanol/water to form vesicular structures, which can be disrupted by simple addition of acid. The nanovesicles are found to encapsulate dye molecules that can be released by the addition of acid as confirmed by fluorescence microscopy and UV studies. The influence of solvent polarity on the morphology of the materials generated from the peptides has been examined systematically, and shows that fibrillar structures are formed in less polar chloroform/petroleum ether mixture and vesicular structures are formed in more polar methanol/water. Single crystal X-ray diffraction studies reveal that while beta-sheet mediated self-assembly leads to the formation of fibrillar structures, the solvated beta-sheet structure leads to the formation of vesicular structures. The results demonstrate that even hydrophobic peptides can generate vesicular structures from polar solvent which may be employed in model studies of complex biological phenomena.

Salts responsive nanovesicles through π-stacking induced self-assembly of backbone modified tripeptides

A set of backbone modified peptides of general formula Boc-Xx-m-ABA-Yy-OMe where m-ABA is meta-aminobenzoic acid and Xx and Yy are natural amino acids such as Phe, Gly, Pro, Leu, Ile, Tyr and Trp etc., are found to self-assemble into soft nanovesicular structures in methanol-water solution (9:1 by v/v). At higher concentration the peptides generate larger vesicles which are formed through fusion of smaller vesicles. The formation of vesicles has been facilitated through the participation of various noncovalent interactions such as aromatic pi-stacking, hydrogen bonding and hydrophobic interactions. Model study indicates that the pi-stacking induced self-assembly, mediated by m-ABA is essential for well structured vesicles formation. The presence of conformationally rigid m-ABA in the backbone of the peptides also helps to form vesicular structures by restricting the conformational entropy. The vesicular structures get disrupted in presence of various salts such as KCl, CaCl(2), N(n-Bu)(4)Br and (NH(4))(2)SO(4) in methanol-water solution. Fluorescence microscopy and UV studies reveal that the soft nanovesicles encapsulate organic dye molecules such as Rhodamine B and Acridine Orange which could be released through salts induced disruption of vesicles.