Ruedi Stoop

An Ontology for Pharmaceutical Ligands and Its Application for in Silico Screening and Library Design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.

Calculation of Lyapunov exponents avoiding spurious elements

Abstract A new approach to evaluate Lyapunov exponents from time series is discussed. With the help of this approach, the number of calculated Lyapunov exponents can be reduced to the physically relevant ones. Calculations in high-dimensional embedding spaces are performed on smaller data bases with improved speed. More direct information is obtained from the dynamics of the underlying dynamical system.

A p-Ge semiconductor experiment showing chaos and hyperchaos

Abstract A p-Ge semiconductor experiment is investigated by the help of both probabilistic and dynamical characterization methods. Dimensions, Lyapunov exponents, and the corresponding scaling functions are calculated. Two exemplary files of data from the p-Ge semiconductor experiment exhibiting spontaneous (i.e., undriven) resistance oscillations in the low-temperature avalanche breakdown are shown to be chaotic and hyperchaotic, respectively. For the first file, we obtained a fractal dimension between two and three and one positive Lyapunov exponent, whereas for the second file we found a fractal dimension between three and four and two positive Lyapunov exponents. Adopting the terminology introduced by Rossler, the behavior corresponding to the latter file is called hyperchaotic. Furthermore, using the language of the thermodynamical formalism, the probabilistic scaling function evaluated for the hyperchaotic state indicates a phase-transition-like-behavior.

Sequential clustering: tracking down the most natural clusters

Sequential superparamagnetic clustering (SSC) is a substantial extension of the superparamagnetic clustering approach (SC). We demonstrate that the novel method is able to master the important problem of inhomogeneous classes in the feature space. By fully exploiting the non-parametric properties of SC, the method is able to find the natural clusters even if they are highly different in shape and density. In such situations, concurrent methods normally fail. We present the results from a fully automated implementation of SSC (applications to chemical data and visual scene analysis) and provide analytical evidence of why the method works.

Neocortical networks of pyramidal neurons: from local locking and chaos to macroscopic chaos and synchronization

We determine the properties of locking of inhibitory and of excitatory synaptic connections with neocortical pyramidal cells. We are able to give an overview of the emerging periodic behaviour, as a function of the perturbation strength. We show that a chaotic response emerges for inhibitory connections on an open set of positive measure of the parameter space. This implies that synchronization on the set of inhibitory connections is possible, with positive probability.

When pyramidal neurons lock, when they respond chaotically, and when they like to synchronize.

We give an overview on the locking properties of perturbed regularly firing pyramidal neurons, as a function of perturbation strength, self-spiking frequency and perturbation frequency. For inhibitory perturbations, instead of locking chaotic response emerges for a whole range of parameters. This suggests that global synchronization on the set of inhibitory connections may easily be achieved.

Sequential superparamagnetic clustering for unbiased classification of high-dimensional chemical data.

For the clustering of chemical structures that are described by the Similog, ISIS count, and ISIS binary fingerprints, we propose a sequential superparamagnetic clustering approach. To appropriately handle nonbinary feature keys, we introduce an extension of the binary Tanimoto similarity measure. In our applications, data sets composed of structures from seven chemically distinct compound classes are evaluated and correctly clustered. The comparison, with results from leading methods, indicates the superiority of our sequential superparamagnetic clustering approach.

Complexity of Dynamics as Variability of Predictability

We construct a complexity measure from first principles, as an average over the “obstruction against prediction” of some observable that can be chosen by the observer. Our measure evaluates the variability of the predictability for characteristic system behaviors, which we extract by means of the thermodynamic formalism. Using theoretical and experimental applications, we show that “complex” and “chaotic” are different notions of perception. In comparison to other proposed measures of complexity, our measure is easily computable, non-divergent for the classical 1-d dynamical systems, and has properties of non-overuniversality. The measure can also be computed for higher-dimensional and experimental systems, including systems composed of different attractors. Moreover, the results of the computations made for classical 1-d dynamical systems imply that it is not the nonhyperbolicity, but the existence of a continuum of characteristic system length scales, that is at the heart of complexity.

Renormalization approach to optimal limiter control in 1-D chaotic systems

Optimal limiter control of chaos in 1-d systems is described by flat-topped maps. When we study the properties of this control by a bifurcation analysis of the latter, we find partial universal behavior. The optimality of the control method is expressed by an exponentially fast control onto selected periodic orbits, making targeting algorithms idle.

Phase synchronization of coupled bursting neurons and the generalized Kuramoto model.

Bursting neurons fire rapid sequences of action potential spikes followed by a quiescent period. The basic dynamical mechanism of bursting is the slow currents that modulate a fast spiking activity caused by rapid ionic currents. Minimal models of bursting neurons must include both effects. We considered one of these models and its relation with a generalized Kuramoto model, thanks to the definition of a geometrical phase for bursting and a corresponding frequency. We considered neuronal networks with different connection topologies and investigated the transition from a non-synchronized to a partially phase-synchronized state as the coupling strength is varied. The numerically determined critical coupling strength value for this transition to occur is compared with theoretical results valid for the generalized Kuramoto model.