Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development

beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi 2, psi 2, phi 3 and psi 3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C-alpha-C-beta vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C-alpha-C-beta vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.

A gradient descent algorithm for minimizing amino acid coupling reactions when synthesizing cyclic-peptide libraries

Combinatorial chemistry has become an invaluable tool in medicinal chemistry for the identification of new drug leads. For example, libraries of predetermined sequences and head-to-tail cyclized peptides are routinely synthesized in our laboratory using the IRORI approach. Such libraries are used as molecular toolkits that enable the development of pharmacophores that define activity and specificity at receptor targets. These libraries can be quite large and difficult to handle, due to physical and chemical constraints imposed by their size. Therefore, smaller sub-libraries are often targeted for synthesis. The number of coupling reactions required can be greatly reduced if the peptides having common amino acids are grouped into the same sub-library (batching). This paper describes a schedule optimizer to minimize the number of coupling reactions by rotating and aligning sequences while simultaneously batching. The gradient descent method thereby reduces the number of coupling reactions required for synthesizing cyclic peptide libraries. We show that the algorithm results in a 75% reduction in the number of coupling reactions for a typical cyclic peptide library.

Carbohydrate-based scaffolds in drug discovery

Carbohydrates have been proven as valuable scaffolds to display pharmocophores and the resulting molecules have demonstrated useful biological activity towards various targets including the somatostatin receptors (SSTR), integrins, HIV-1 protease, matrix metalloproteinases (MMP), multidrug resistance-associated protein (MRP), and as RNA binders. Carbohydrate-based compounds have also shown antibacterial and herbicidal activity.

Quantitative analysis and characterization of biofunctionalized fluorescent silica particles

A strategy for the production and subsequent characterization of biofunctionalized silica particles is presented. The particles were engineered to produce a bifunctional material capable of both (a) the attachment of fluorescent dyes for particle encoding and (b) the sequential modification of the surface of the particles to couple oligonucleotide probes. A combination of microscopic and analytical methods is implemented to demonstrate that modification of the particles with 3-aminopropyl trimethoxysilane results in an even distribution of amine groups across the particle surface. Evidence is provided to indicate that there are negligible interactions between the bound fluorescent dyes and the attached biomolecules. A unique approach was adopted to provide direct quantification of the oligonucleotide probe loading on the particle surface through X-ray photoelectron spectroscopy, a technique which may have a major impact for current researchers and users of bead-based technologies. A simple hybridization assay showing high sequence specificity is included to demonstrate the applicability of these particles to DNA screening.

Tailoring surface properties to build colloidal diagnostic devices: Controlling interparticle associations

The ability to control the surface properties and subsequent colloidal stability of dispersed particles has widespread applicability in many fields. Sub-micrometer fluorescent silica particles (reporters) can be used to actively encode the combinatorial synthesis of peptide libraries through interparticle association. To achieve these associations, the surface chemistry of the small fluorescent silica reporters is tailored to encourage robust adhesion to large silica microparticles onto which the peptides are synthesized. The interparticle association must withstand a harsh solvent environment multiple synthetic and washing procedures, and biological screening buffers. The encoded support beads were exposed to different solvents used for peptide synthesis, and different solutions used for biological screening including phosphate buffered saline (PBS), 2-[N-morpholino]ethane sulfonic acid (VIES) and a mixture of MES and N-(3-dimethyl-aminopropyl)-N'-ethylcarbodiimide (EDC). The number of reporters remaining adhered to the support bead was quantified after each step. The nature of the associations were explored and tested to optimize the efficiency of these phenomena. Results presented illustrate the influence of the surface functionality and polyelectrolyte modification of the reporters. These parameters were investigated through zeta potential and X-ray photoelectron spectroscopy.

Toy model for a relational formulation of quantum theory

In the absence of an external frame of reference-i.e., in background independent theories such as general relativity-physical degrees of freedom must describe relations between systems. Using a simple model, we investigate how such a relational quantum theory naturally arises by promoting reference systems to the status of dynamical entities. Our goal is twofold. First, we demonstrate using elementary quantum theory how any quantum mechanical experiment admits a purely relational description at a fundamental. Second, we describe how the original non-relational theory approximately emerges from the fully relational theory when reference systems become semi-classical. Our technique is motivated by a Bayesian approach to quantum mechanics, and relies on the noiseless subsystem method of quantum information science used to protect quantum states against undesired noise. The relational theory naturally predicts a fundamental decoherence mechanism, so an arrow of time emerges from a time-symmetric theory. Moreover, our model circumvents the problem of the collapse of the wave packet as the probability interpretation is only ever applied to diagonal density operators. Finally, the physical states of the relational theory can be described in terms of spin networks introduced by Penrose as a combinatorial description of geometry, and widely studied in the loop formulation of quantum gravity. Thus, our simple bottom-up approach (starting from the semiclassical limit to derive the fully relational quantum theory) may offer interesting insights on the low energy limit of quantum gravity.

A novel suite of cyclotides from Viola odorata: sequence variation and the implications for structure, function and stability

Cyclotides are a fascinating family of plant-derived peptides characterized by their head-to-tail cyclized backbone and knotted arrangement of three disulfide bonds. This conserved structural architecture, termed the CCK (cyclic cystine knot), is responsible for their exceptional resistance to thermal, chemical and enzymatic degradation. Cyclotides have a variety of biological activities, but their insecticidal activities suggest that their primary function is in plant defence. In the present study, we determined the cyclotide content of the sweet violet Viola odorata, a member of the Violaceae family. We identified 30 cyclotides from the aerial parts and roots of this plant, 13 of which are novel sequences. The new sequences provide information about the natural diversity of cyclotides and the role of particular residues in defining structure and function. As many of the biological activities of cyclotides appear to be associated with membrane interactions, we used haemolytic activity as a marker of bioactivity for a selection of the new cyclotides. The new cyclotides were tested for their ability to resist proteolysis by a range of enzymes and, in common with other cyclotides, were completely resistant to trypsin, pepsin and thermolysin. The results show that while biological activity varies with the sequence, the proteolytic stability of the framework does not, and appears to be an inherent feature of the cyclotide framework. The structure of one of the new cyclotides, cycloviolacin O14, was determined and shown to contain the CCK motif. This study confirms that cyclotides may be regarded as a natural combinatorial template that displays a variety of peptide epitopes most likely targeted to a range of plant pests and pathogens.

Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms

The aim of this study was to identify a set of genetic polymorphisms that efficiently divides methicillin-resistant Staphylococcus aureus (MRSA) strains into groups consistent with the population structure. The rationale was that such polymorphisms could underpin rapid real-time PCR or low-density array-based methods for monitoring MRSA dissemination in a cost-effective manner. Previously, the authors devised a computerized method for identifying sets of single nucleoticle polymorphisms (SNPs) with high resolving power that are defined by multilocus sequence typing (MLST) databases, and also developed a real-time PCR method for interrogating a seven-member SNP set for genotyping S. aureus. Here, it is shown that these seven SNPs efficiently resolve the major MRSA lineages and define 27 genotypes. The SNP-based genotypes are consistent with the MRSA population structure as defined by eBURST analysis. The capacity of binary markers to improve resolution was tested using 107 diverse MRSA isolates of Australian origin that encompass nine SNP-based genotypes. The addition of the virulence-associated genes cna, pvl and bbplsdrE, and the integrated plasmids pT181, p1258 and pUB110, resolved the nine SNP-based genotypes into 21 combinatorial genotypes. Subtyping of the SCCmec locus revealed new SCCmec types and increased the number of combinatorial genotypes to 24. It was concluded that these polymorphisms provide a facile means of assigning MRSA isolates into well-recognized lineages.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Dartboard arrangements with a concave penalty function

This paper investigates combinatorial arrangements of the dartboard to maximize a penalty function derived from the differences of adjacent sectors. The particular penalty function is constructed by summing the absolute differences of neighbouring sectors raised to a power between zero and one. The arrangement to give the maximum penalty is found

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Visualization of molecular fingerprints

A visualization plot of a data set of molecular data is a useful tool for gaining insight into a set of molecules. In chemoinformatics, most visualization plots are of molecular descriptors, and the statistical model most often used to produce a visualization is principal component analysis (PCA). This paper takes PCA, together with four other statistical models (NeuroScale, GTM, LTM, and LTM-LIN), and evaluates their ability to produce clustering in visualizations not of molecular descriptors but of molecular fingerprints. Two different tasks are addressed: understanding structural information (particularly combinatorial libraries) and relating structure to activity. The quality of the visualizations is compared both subjectively (by visual inspection) and objectively (with global distance comparisons and local k-nearest-neighbor predictors). On the data sets used to evaluate clustering by structure, LTM is found to perform significantly better than the other models. In particular, the clusters in LTM visualization space are consistent with the relationships between the core scaffolds that define the combinatorial sublibraries. On the data sets used to evaluate clustering by activity, LTM again gives the best performance but by a smaller margin. The results of this paper demonstrate the value of using both a nonlinear projection map and a Bernoulli noise model for modeling binary data.