Susanne von Grafenstein

Heteroaromatic π‑Stacking Energy Landscapes

In this study we investigate π-stacking interactions of a variety of aromatic heterocycles with benzene using dispersion corrected density functional theory. We calculate extensive potential energy surfaces for parallel-displaced interaction geometries. We find that dispersion contributes significantly to the interaction energy and is complemented by a varying degree of electrostatic interactions. We identify geometric preferences and minimum interaction energies for a set of 13 5- and 6-membered aromatic heterocycles frequently encountered in small drug-like molecules. We demonstrate that the electrostatic properties of these systems are a key determinant for their orientational preferences. The results of this study can be applied in lead optimization for the improvement of stacking interactions, as it provides detailed energy landscapes for a wide range of coplanar heteroaromatic geometries. These energy landscapes can serve as a guide for ring replacement in structure-based drug design.

Cleavage Entropy as Quantitative Measure of Protease Specificity

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.

The impact of nitration on the structure and immunogenicity of the major birch pollen allergen Bet v 1.0101.

Allergy prevalence has increased in industrialized countries. One contributing factor could be pollution, which can cause nitration of allergens exogenously (in the air) or endogenously (in inflamed lung tissue). We investigated the impact of nitration on both the structural and immunological behavior of the major birch pollen allergen Bet v 1.0101 to determine whether nitration might be a factor in the increased incidence of allergy. Bet v 1.0101 was nitrated with tetranitromethane. Immune effects were assessed by measuring the proliferation of specific T-cell lines (TCLs) upon stimulation with different concentrations of nitrated and unmodified allergen, and by measurement of cytokine release of monocyte-derived dendritic cells (moDCs) and primary DCs (primDCs) stimulated with nitrated versus unmodified allergen. HPLC-MS, crystallography, gel electrophoresis, amino acid analysis, size exclusion chromatography and molecular dynamics simulation were performed to characterize structural changes after nitration of the allergen. The proliferation of specific TCLs was higher upon stimulation with the nitrated allergen in comparison to the unmodified allergen. An important structural consequence of nitration was oligomerization. Moreover, analysis of the crystal structure of nitrated Bet v 1.0101 showed that amino acid residue Y83, located in the hydrophobic cavity, was nitrated to 100%. Both moDCs and primDCs showed decreased production of TH1-priming cytokines, thus favoring a TH2 response. These results implicate that nitration of Bet v 1.0101 might be a contributing factor to the observed increase in birch pollen allergy, and emphasize the importance of protein modifications in understanding the molecular basis of allergenicity.

Dynamic Regulation of Phenylalanine Hydroxylase by Simulated Redox Manipulation

Recent clinical studies revealed increased phenylalanine levels and phenylalanine to tyrosine ratios in patients suffering from infection, inflammation and general immune activity. These data implicated down-regulation of activity of phenylalanine hydroxylase by oxidative stress upon in vivo immune activation. Though the structural damage of oxidative stress is expected to be comparably small, a structural rationale for this experimental finding was lacking. Hence, we investigated the impact of side chain oxidation at two vicinal cysteine residues on local conformational flexibility in the protein by comparative molecular dynamics simulations. Analysis of backbone dynamics revealed a highly flexible loop region (Tyr138-loop) in proximity to the active center of phenylalanine hydroxylase. We observed elevated loop dynamics in connection with a loop movement towards the active site in the oxidized state, thereby partially blocking access for the substrate phenylalanine. These findings were confirmed by extensive replica exchange molecular dynamics simulations and serve as a first structural explanation for decreased enzyme turnover in situations of oxidative stress.

Substrate-driven mapping of the degradome by comparison of sequence logos.

Sequence logos are frequently used to illustrate substrate preferences and specificity of proteases. Here, we employed the compiled substrates of the MEROPS database to introduce a novel metric for comparison of protease substrate preferences. The constructed similarity matrix of 62 proteases can be used to intuitively visualize similarities in protease substrate readout via principal component analysis and construction of protease specificity trees. Since our new metric is solely based on substrate data, we can engraft the protease tree including proteolytic enzymes of different evolutionary origin. Thereby, our analyses confirm pronounced overlaps in substrate recognition not only between proteases closely related on sequence basis but also between proteolytic enzymes of different evolutionary origin and catalytic type. To illustrate the applicability of our approach we analyze the distribution of targets of small molecules from the ChEMBL database in our substrate-based protease specificity trees. We observe a striking clustering of annotated targets in tree branches even though these grouped targets do not necessarily share similarity on protein sequence level. This highlights the value and applicability of knowledge acquired from peptide substrates in drug design of small molecules, e.g., for the prediction of off-target effects or drug repurposing. Consequently, our similarity metric allows to map the degradome and its associated drug target network via comparison of known substrate peptides. The substrate-driven view of protein-protein interfaces is not limited to the field of proteases but can be applied to any target class where a sufficient amount of known substrate data is available.

Butyltin(IV) Benzoates: Inhibition of Thioredoxin Reductase, Tumor Cell Growth Inhibition, and Interactions with Proteins

Thioredoxin reductase (TrxR) is overexpressed in cancer cells and is therefore a putative cancer target. Inhibition of this enzyme is considered an important strategy for the development of new chemotherapeutic agents with a specific mechanism of action. Organotin compounds have been described as experimental antitumor agents, yet their mechanism of action remains largely unknown. Based on the outcome of a virtual screening study, various di‐ and tri‐n‐butyltin(IV) carboxylates were synthesized, and their biological properties were evaluated. All synthesized compounds were able to inhibit TrxR selectively within the micromolar range and showed potent antitumor activity against HT‐29 and MCF‐7 cancer cell lines. Moreover, tin(IV) organometallics were found to strongly induce apoptosis in the BJAB lymphoma cell line. Mass spectrometry and atomic absorption spectroscopy experiments revealed metal binding to proteins, and efficient cellular uptake was observed using a di‐n‐butyltin(IV) complex as an example.

Entropy from state probabilities: hydration entropy of cations.

Entropy is an important energetic quantity determining the progression of chemical processes. We propose a new approach to obtain hydration entropy directly from probability density functions in state space. We demonstrate the validity of our approach for a series of cations in aqueous solution. Extensive validation of simulation results was performed. Our approach does not make prior assumptions about the shape of the potential energy landscape and is capable of calculating accurate hydration entropy values. Sampling times in the low nanosecond range are sufficient for the investigated ionic systems. Although the presented strategy is at the moment limited to systems for which a scalar order parameter can be derived, this is not a principal limitation of the method. The strategy presented is applicable to any chemical system where sufficient sampling of conformational space is accessible, for example, by computer simulations.

Interface dynamics explain assembly dependency of influenza neuraminidase catalytic activity

Influenza virus neuraminidase (iNA) is a homotetrameric surface protein of the influenza virus and an established target for antiviral drugs. In contrast to neuraminidases (NAs) of other biological systems (non-iNAs), enzymatic activity of iNA is only observed in a quaternary assembly and iNA needs the tetramerization to mediate enzymatic activity. Obviously, differences on a molecular level between iNA and non-iNAs are responsible for this intriguing observation. Comparison between protein structures and multiple sequence alignment allow the identification of differences in amino acid composition in crucial regions of the enzyme, such as next to the conserved D151 and the 150-loop. These differences in amino acid sequence and protein tetramerization are likely to alter the dynamics of the system. Therefore, we performed molecular dynamics simulations to investigate differences in the molecular flexibility of monomers, dimers, and tetramers of iNAs of subtype N1 (avian 2004, pandemic 1918 and pandemic 2009 iNA) and as comparison the non-iNA monomer from Clostridium perfringens. We show that conformational transitions of iNA are crucially influenced by its assembly state. The protein–protein interface induces a complex hydrogen-bonding network between the 110-helix and the 150-loop, which consequently stabilizes the structural arrangement of the binding site. Therefore, we claim that these altered dynamics are responsible for the dependence of iNA’s catalytic activity on the tetrameric assembly. Only the tetramerization-induced balance between stabilization and altered local flexibility in the binding site provides the appropriate arrangement of key residues for iNA’s catalytic activity.

Independent Metrics for Protein Backbone and Side-Chain Flexibility: Time Scales and Effects of Ligand Binding.

Conformational dynamics are central for understanding biomolecular structure and function, since biological macromolecules are inherently flexible at room temperature and in solution. Computational methods are nowadays capable of providing valuable information on the conformational ensembles of biomolecules. However, analysis tools and intuitive metrics that capture dynamic information from in silico generated structural ensembles are limited. In standard work-flows, flexibility in a conformational ensemble is represented through residue-wise root-mean-square fluctuations or B-factors following a global alignment. Consequently, these approaches relying on global alignments discard valuable information on local dynamics. Results inherently depend on global flexibility, residue size, and connectivity. In this study we present a novel approach for capturing positional fluctuations based on multiple local alignments instead of one single global alignment. The method captures local dynamics within a structural ensemble independent of residue type by splitting individual local and global degrees of freedom of protein backbone and side-chains. Dependence on residue type and size in the side-chains is removed via normalization with the B-factors of the isolated residue. As a test case, we demonstrate its application to a molecular dynamics simulation of bovine pancreatic trypsin inhibitor (BPTI) on the millisecond time scale. This allows for illustrating different time scales of backbone and side-chain flexibility. Additionally, we demonstrate the effects of ligand binding on side-chain flexibility of three serine proteases. We expect our new methodology for quantifying local flexibility to be helpful in unraveling local changes in biomolecular dynamics.

Identification of Novel Liver X Receptor Activators by Structure-Based Modeling

Liver X receptors (LXRs) are members of the nuclear receptor family. Activators of LXRs are of high pharmacological interest as LXRs regulate cholesterol, fatty acid, and carbohydrate metabolism as well as inflammatory processes. On the basis of different X-ray crystal structures, we established a virtual screening workflow for the identification of novel LXR modulators. A two-step screening concept to identify active compounds included 3D-pharmacophore filters and rescoring by shape alignment. Eighteen virtual hits were tested in vitro applying a reporter gene assay, where concentration-dependent activity was proven for four novel lead structures. The most active compound 10, a 1,4-naphthochinone, has an estimated EC50 of around 5 μM.