Optimized nanospherical layered alternating metal-dielectric probes for optical sensing.

Multishell nanospheres have been proposed as a class of layered alternating metal-dielectric probes (LAMPs) that can greatly enhance sensitivity and multiplexing capabilities of optical molecular imaging . Here we theoretically demonstrate that the interplasmonic coupling within these spheres and hence their spectral responses can be tuned by a rational selection of layer thicknesses. As a proof-of-concept, layered Mie theory calculations of near- and far-field characteristics followed by a genetic algorithm-based selection are presented for gold-silica, silver-silica and copper-silica LAMPs. The results demonstrate that the optical tunability available allows for design of application (excitation wavelength)-specific probes of different sizes. The tunability further increases with number of layers and within a particular allowable probe size provides for structures with distinct resonances at longer wavelengths. The concept of scaling internal field resonances is also shown theoretically and the range over which the magnitudes can be tuned are presented.

Optimally designed nanolayered metal-dielectric particles as probes for massively multiplexed and ultrasensitive molecular assays.

An outstanding challenge in biomedical sciences is to devise a palette of molecular probes that can enable simultaneous and quantitative imaging of tens to hundreds of species down to ultralow concentrations. Addressing this need using surface-enhanced Raman scattering-based probes is potentially possible. Here, we theorize a rational design and optimization strategy to obtain reproducible probes using nanospheres with alternating metal and reporter-filled dielectric layers. The isolation of reporter molecules from metal surfaces suppresses chemical enhancement, and consequently signal enhancements are determined by electromagnetic effects alone. This strategy synergistically couples interstitial surface plasmons and permits the use of almost any molecule as a reporter by eliminating the need for surface attachment. Genetic algorithms are employed to optimize the layer dimensions to provide controllable enhancements exceeding 11 orders of magnitude and of single molecule sensitivity for nonresonant and resonant reporters, respectively. The strategy also provides several other opportunities, including a facile route to tuning the response of these structures to be spectrally flat and localization of the enhancement within a specific volume inside or outside the probe. The spectrally uniform enhancement for multiple excitation wavelengths and for different shifts enables generalized probes, whereas enhancement tuning permits a large dynamic range by suppression of enhancements from outside the probe. Combined, these theoretical calculations open the door for a set of reproducible and robust probes with controlled sensitivity for molecular sensing over a concentration range of over 20 orders of magnitude.

Invariability of central metabolic flux distribution in Shewanella oneidensis MR-1 under environmental or genetic perturbations.

An environmentally important bacterium with versatile respiration, Shewanella oneidensis MR-1, displayed significantly different growth rates under three culture conditions: minimal medium (doubling time approximately 3 h), salt stressed minimal medium (doubling time approximately 6 h), and minimal medium with amino acid supplementation (doubling time approximately 1.5 h). (13)C-based metabolic flux analysis indicated that fluxes of central metabolic reactions remained relatively constant under the three growth conditions, which is in stark contrast to the reported significant changes in the transcript and metabolite profiles under various growth conditions. Furthermore, 10 transposon mutants of S. oneidensis MR-1 were randomly chosen from a transposon library and their flux distributions through central metabolic pathways were revealed to be identical, even though such mutational processes altered the secondary metabolism, for example, glycine and C1 (5,10-Me-THF) metabolism.

Metabolic flux analysis of Shewanella spp. reveals evolutionary robustness in central carbon metabolism.

Shewanella spp. are a group of facultative anaerobic bacteria widely distributed in marine and freshwater environments. In this study, we profiled the central metabolic fluxes of eight recently sequenced Shewanella species grown under the same condition in minimal medium with [3-13C] lactate. Although the tested Shewanella species had slightly different growth rates (0.23-0.29 h(-1)) and produced different amounts of acetate and pyruvate during early exponential growth (pseudo-steady state), the relative intracellular metabolic flux distributions were remarkably similar. This result indicates that Shewanella species share similar regulation in regard to central carbon metabolic fluxes under steady growth conditions: the maintenance of metabolic robustness is not only evident in a single species under genetic perturbations (Fischer and Sauer, 2005; Nat Genet 37(6):636-640), but also observed through evolutionary related microbial species. This remarkable conservation of relative flux profiles through phylogenetic differences prompts us to introduce the concept of metabotype as an alternative scheme to classify microbial fluxomics. On the other hand, Shewanella spp. display flexibility in the relative flux profiles when switching their metabolism from consuming lactate to consuming pyruvate and acetate.

Evolutionary computation and multimodal search: a good combination to tackle molecular diversity in the field of peptide design.

The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.

Automated global structure extraction for effective local building block processing in XCS.

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

ENPDA: an evolutionary structure-based de novo peptide design algorithm.

One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules.

A new screening procedure is described that uses docking calculations to design enhanced agonist peptides that bind to major histocompatibility complex (MHC) class I receptors. The screening process proceeds via single mutations of one amino acid at the positions that directly interact with the MHC receptor. The energetic and structural effects of these mutations have been studied using fragments of the original ligand that vary in length. The results of these docking studies indicate that the mutant affinity ranking of long peptides can be practically reproduced with a screening approach performed using fragments of six residues. Fragments of four and five residues could mimic, in some cases, the structural arrangement of the side chains of the full-length peptide. We have compared the structural and energetic results of the docking calculations with experimental data using three unrelated ligand peptides that differ greatly in their affinity for the MHC complex. Analysis of the affinity of the fragments led to the identification of three important parameters in the construction of fragments that mimic the structural and energetic properties of the full-length ligand: the length of the fragment; its intermolecular energy; and the number and localization, internal or terminal, of the anchor residues. The results of this new peptide-design methodology have been applied to suggest new peptides derived from the MUC1-8 peptide that could be used as murine vaccines that trigger the immune response through the MHC class I protein H-2K(b).

Evolutionary combinatorial chemistry, a novel tool for SAR studies on peptide transport across the blood-brain barrier. Part 2. Design, synthesis and evaluation of a first generation of peptides.

The use of high-throughput methods in drug discovery allows the generation and testing of a large number of compounds, but at the price of providing redundant information. Evolutionary combinatorial chemistry combines the selection and synthesis of biologically active compounds with artificial intelligence optimization methods, such as genetic algorithms (GA). Drug candidates for the treatment of central nervous system (CNS) disorders must overcome the blood-brain barrier (BBB). This paper reports a new genetic algorithm that searches for the optimal physicochemical properties for peptide transport across the blood-brain barrier. A first generation of peptides has been generated and synthesized. Due to the high content of N-methyl amino acids present in most of these peptides, their syntheses were especially challenging due to over-incorporations, deletions and DKP formations. Distinct fragmentation patterns during peptide cleavage have been identified. The first generation of peptides has been studied by evaluation techniques such as immobilized artificial membrane chromatography (IAMC), a cell-based assay, log Poctanol/water calculations, etc. Finally, a second generation has been proposed.

Bounding the effect of noise in multiobjective learning classifier systems.

This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.