Marcus Weber

Multivalency as a Chemical Organization and Action Principle

Multivalent interactions can be applied universally for a targeted strengthening of an interaction between different interfaces or molecules. The binding partners form cooperative, multiple receptor-ligand interactions that are based on individually weak, noncovalent bonds and are thus generally reversible. Hence, multi- and polyvalent interactions play a decisive role in biological systems for recognition, adhesion, and signal processes. The scientific and practical realization of this principle will be demonstrated by the development of simple artificial and theoretical models, from natural systems to functional, application-oriented systems. In a systematic review of scaffold architectures, the underlying effects and control options will be demonstrated, and suggestions will be given for designing effective multivalent binding systems, as well as for polyvalent therapeutics.

A coarse graining method for the identification of transition rates between molecular conformations

The coarse graining method to be advocated in this paper consists of two main steps. First, the propagation of an ensemble of molecular states is described as a Markov chain by a transition probability matrix in a finite state space. Second, we obtain metastable conformations by an aggregation of variables via Robust Perron Cluster Analysis (PCCA+). Up to now, it has been an open question as to how this coarse graining in space can be transformed to a coarse graining of the Markov chain while preserving the essential dynamic information. In this article, we construct a coarse matrix that is the correct propagator in the space of conformations. This coarse graining procedure carries over to rate matrices and allows to extract transition rates between molecular conformations. This approach is based on the fact that PCCA+ computes molecular conformations as linear combinations of the dominant eigenvectors of the transition matrix.

DNA-programmed spatial screening of carbohydrate–lectin interactions

A wide range of multivalent scaffolds was assembled by using only five different PNA oligomers and various DNA templates. The flexibility of the PNA–DNA duplexes could be increased by introducing nick-sites and partially unpaired regions, as confirmed by MD simulations. The self-organized glyco-assemblies were used in a spatial screening of accessible carbohydrate binding sites in the Erythrina cristagalli lectin (ECL). This systematic investigation revealed a distance dependence which is in agreement with the crystal structure analysis.

Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification

Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster Analysis (PCCA+), which delivers a fuzzy clustering. Originally developed for clustering the state space of Markov chains, the method became popular as a versatile tool for general data classification problems. The robustness of PCCA+, however, cannot be explained by previous perturbation results, because the matrices in typical applications do not comply with the two main requirements: reversibility and nearly decomposability. We therefore demonstrate in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states.

DNA-controlled bivalent presentation of ligands for the estrogen receptor.

The assembly of DNA complexes proceeds according to known rules. Thus, the mutual recognition of DNA conjugates can be used for the precise positioning of functional groups. For example, chromophores, metals, catalytic units, nanoparticles, fluorophores and even proteins have been arranged at well-defined distances by means of DNA hybridization. Until recently, the main interest was focused on issues within materials science as well as on the immobilization of biomolecules. We and others assumed that the ability to position functional units at defined distances could also be used to address biological problems. According to this, DNA may serve as a molecular ruler to determine the distance between binding pockets in biological receptors. Due to self-assembly of the DNA complex the rapid spatial screening of a receptor can be doen with minor synthetic effort. In this approach, the ligand of a biological receptor is covalently attached to an oligonucleotide (Figure 1a). The binding of two or more oligonucleotide–ligand conjugates to a template strand provides bior multivalent DNA–ligand conjugates. The distance between the two biologically active ligands can be readily adjusted by varying of the template strand. Herein we demonstrate, for the first time, the DNAcontrolled presentation of small molecules in the spatial screening of a protein receptor. We demonstrate the advantages conferred by DNA spacers by examining a well-studied nuclear receptor, the estrogen receptor, and by comparison with commonly applied oligoethyleneglycol spacers. The estrogen receptor (ER) is activated by the hormone estradiol and is involved in the regulation of gene expression. It is assumed that the formation of dimers is essential for the natural function of the receptor (Figure 1b). The dimerization constant is in the subnanomolar range, yet it must be considered that ligand binding can influence the dimerization equilibrium. The selective estrogen receptor modulators (SERMs) hexestrol, raloxifene, and 4-hydroxytamoxifene stabilize the receptor dimer and were thus deemed suitable for the spatial screening of the ER. The synthesis of the SERM–oligonucleotide conjugates was achieved by introducing the alkyne-modified uridine building block X during automated DNA synthesis (Scheme 1). The SERMs were equipped with azido functions to enable the covalent attachment to the oligonucleotides ODN-X by the Cu-catalyzed 1,3-dipolar cycloaddition. The resulting conjugates ODN-XR were obtained in 30–70% yield. The affinity of the oligonucleotide–SERM complexes to the estrogen receptor (ER-a) was assessed by means of the HitHunter assay. The conjugation of hexestrol (Hex) with an oligonucleotide diminished the affinity by several orders of magnitude (Figure 2). This result appears plausible because in the structure of the ER in complex with agonists such as hexestrol the binding pocket is nearly closed (Figure S33 in the Supporting Information). In contrast, the estrogen analogues raloxifene (Ral) and 4-hydroxytamoxifene (Tam) Figure 1. a) Bivalent presentation of estrogen receptor ligands (L) on ternary DNA complexes. b) Crystal structure of the ligand binding domain of the estrogen receptor (PDB ID: 1ERR) in complex with raloxifene (orange). The nitrogen atoms of raloxifene (blue) are 35 apart.

Metastable β‐Bi2O3 Nanoparticles with Potential for Photocatalytic Water Purification Using Visible Light Irradiation

Photocatalytic studies under visible light irradiation using nanosized β-Bi2O3 are reported. β-Bi2O3 nanoparticles are prepared starting from the well-defined bismuth oxido cluster [Bi38O45(OMc)24(DMSO)9]⋅2 DMSO⋅7 H2O (OMc=O2CC3H5) using a straightforward hydrolysis and annealing protocol. Powder X-ray diffraction studies, transmission electron microscopy, diffuse reflectance UV/Vis spectroscopy and nitrogen adsorption measurements (using the Brunauer–Emmett–Teller (BET) theory) are used for the characterization of the as-prepared β-Bi2O3. By time-dependent annealing, the crystallite size can be controlled between (17±2) nm and (45±5) nm with BET surface areas of 7 to 29 m2 g−1. The indirect band gap of the as-prepared β-Bi2O3 amounts to (2.15±0.05) eV. The decomposition rates for rhodamine B (RhB) solutions are in the range of 2.46×10−5 to 4.01×10−4 s−1 and depend on the crystallite size, amount of catalyst and concentration of RhB. Photocorrosion experiments have shown the formation of Bi2O2CO3 after several catalytic cycles. However, the catalyst can be recycled to phase-pure β-Bi2O3 nanoparticles by annealing for one hour under argon atmosphere at 380 °C. Furthermore, the photocatalytic activity of as-prepared β-Bi2O3 nanoparticles for the decomposition of phenol, 4-chlorophenol, 2,4-dichlorphenol, 4-nitrophenol, triclosan and ethinyl estradiol is demonstrated.

A nontoxic pain killer designed by modeling of pathological receptor conformations

A pain killer without side effects Opioids are very strong and effective pain killers. However, they also have a range of well-known side effects and can cause addiction. Painful conditions such as inflammation or trauma are often associated with localized tissue acidification. Spahn et al. designed a novel opioid receptor agonist that, unlike clinically used opioids, best activates the receptors in such acidified tissues. In rat models of inflammatory pain, the new drug exerted strong pain relief essentially without the side effects of standard opioids. Science, this issue p. 966 A novel opioid selectively activates peripheral opioid receptors only in inflamed tissue. Indiscriminate activation of opioid receptors provides pain relief but also severe central and intestinal side effects. We hypothesized that exploiting pathological (rather than physiological) conformation dynamics of opioid receptor-ligand interactions might yield ligands without adverse actions. By computer simulations at low pH, a hallmark of injured tissue, we designed an agonist that, because of its low acid dissociation constant, selectively activates peripheral μ-opioid receptors at the source of pain generation. Unlike the conventional opioid fentanyl, this agonist showed pH-sensitive binding, heterotrimeric guanine nucleotide–binding protein (G protein) subunit dissociation by fluorescence resonance energy transfer, and adenosine 3′,5′-monophosphate inhibition in vitro. It produced injury-restricted analgesia in rats with different types of inflammatory pain without exhibiting respiratory depression, sedation, constipation, or addiction potential.

Applications of the cross-entropy method to importance sampling and optimal control of diffusions

We study the cross-entropy method for diffusions. One of the results is a versatile cross-entropy algorithm that can be used to design efficient importance sampling strategies for rare events or to solve optimal control problems. The approach is based on the minimization of a suitable cross-entropy functional, with a parametric family of exponentially tilted probability distributions. We illustrate the new algorithm with several numerical examples and discuss algorithmic issues and possible extensions of the method.

Stable computation of probability densities for metastable dynamical systems

Whenever the invariant stationary density of metastable dynamical systems decomposes into almost invariant partial densities, its computation as eigenvector of some transition probability matrix is an ill-conditioned problem. In order to avoid this computational difficulty, we suggest applying an aggregation/disaggregation method which addresses only well-conditioned subproblems and thus results in a stable algorithm. In contrast to existing methods, the aggregation step is done via a sampling algorithm which covers only small patches of the sampling space. Finally, the theoretical analysis is illustrated by two biomolecular examples.

Robust perron cluster analysis for various applications in computational life science

In the present paper we explain the basic ideas of Robust Perron Cluster Analysis (PCCA+) and exemplify the different application areas of this new and powerful method. Recently, Deuflhard and Weber [5] proposed PCCA+ as a new cluster algorithm in conformation dynamics for computational drug design. This method was originally designed for the identification of almost invariant subsets of states in a Markov chain. As an advantage, PCCA+ provides an indicator for the number of clusters. It turned out that PCCA+ can also be applied to other problems in life science. We are going to show how it serves for the clustering of gene expression data stemming from breast cancer research [20]. We also demonstrate that PCCA+ can be used for the clustering of HIV protease inhibitors corresponding to their activity. In theoretical chemistry, PCCA+ is applied to the analysis of metastable ensembles in monomolecular kinetics, which is a tool for RNA folding [21].