The sclerostin-neutralizing antibody AbD09097 recognizes an epitope adjacent to sclerostin's binding site for the Wnt co-receptor LRP6.

The glycoprotein sclerostin has been identified as a negative regulator of bone growth. It exerts its function by interacting with the Wnt co-receptor LRP5/6, blocks the binding of Wnt factors and thereby inhibits Wnt signalling. Neutralizing anti-sclerostin antibodies are able to restore Wnt activity and enhance bone growth thereby presenting a new osteoanabolic therapy approach for diseases such as osteoporosis. We have generated various Fab antibodies against human and murine sclerostin using a phage display set-up. Biochemical analyses have identified one Fab developed against murine sclerostin, AbD09097 that efficiently neutralizes sclerostin's Wnt inhibitory activity. In vitro interaction analysis using sclerostin variants revealed that this neutralizing Fab binds to sclerostin's flexible second loop, which has been shown to harbour the LRP5/6 binding motif. Affinity maturation was then applied to AbD09097, providing a set of improved neutralizing Fab antibodies which particularly bind human sclerostin with enhanced affinity. Determining the crystal structure of AbD09097 provides first insights into how this antibody might recognize and neutralize sclerostin. Together with the structure-function relationship derived from affinity maturation these new data will foster the rational design of new and highly efficient anti-sclerostin antibodies for the therapy of bone loss diseases such as osteoporosis.

A structurally flexible triazolate-based metal-organic framework featuring coordinatively unsaturated copper(i) sites.

The novel metal-organic framework CFA-8 (Coordination Framework Augsburg University-8), [Cu2(tqpt)], containing the organic linker H2-tqpt {H2-tqpt = 6,6,14,14-tetramethyl-6,14-dihydroquinoxalino[2,3-b]phenazinebis-triazole}, has been synthesized. Reaction of H2-tqpt and anhydrous CuCl2 in N,N-dimethylacetamide (DMA) yields CFA-8 as orange crystals with lenticular shape. This framework shows a reversible breathing effect and is robust upon solvent removal. It has been characterized by single-crystal and powder X-ray diffraction, TGA, IR spectroscopy and gas sorption measurements. CO adsorption isotherms show that Cu(i) sites in this framework are able to bind carbon monoxide forming a weak complex which has been additionally characterized by IR spectroscopy and synchrotron powder X-ray diffraction measurements.

A rule-based expert system for chemical prioritization using effects-based chemical categories.

A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically transparent and meet the needs of a specific application, i.e. predict for all chemicals within two well-defined inventories (industrial chemicals used as pesticide inerts and antimicrobial pesticides). These chemicals all lack structural features associated with high affinity binders and thus any binding should be low affinity. Similar to the high-quality fathead minnow database upon which Veith QSARs were built, the ERES was derived from what has been termed gold standard data, systematically collected in assays optimized to detect even low affinity binding and maximizing confidence in the negatives determinations. The resultant logic-based decision tree ERES, determined to be a robust model, contains seven major nodes with multiple effects-based chemicals categories within each. Predicted results are presented in the context of empirical data within local chemical structural groups facilitating informed decision-making. Even using optimized detection assays, the ERES applied to two inventories of >600 chemicals resulted in only ~5% of the chemicals predicted to bind ER.

Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals.

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.

Strategic selection of chemicals for testing. Part I. Functionalities and performance of basic selection methods.

To develop quantitative structure-activity relationships (QSAR) models capable of predicting adverse effects for large chemical inventories and diverse structures, an interactive approach is presented that includes testing of strategically selected chemicals to expand the scope of a preliminary model to cover a target inventory. The goal of chemical selection in this context is to make the testing more effective in terms of adding maximal new structural information to the predictive model with minimal testing. The aim of this paper is to describe a set of algorithmic solutions and modelling techniques that can be used to efficiently select chemicals for testing to achieve a variety of goals. One purpose of chemical selection is to refine the model thus extending our knowledge about the modelled subject. Alternatively, the selection of chemicals for testing could be targeted at achieving a more adequate structural representation of a specific universe of untested chemicals to extend the model applicability domain on each subsequent step of model development. The chemical selection tools are collectively provided in a software package referred to as ChemPick. The system also allows the visualisation of chemical inventories and training sets in multidimensional (two and three dimensions) descriptor space. The software environment allows one or more datasets to be simultaneously loaded in a three-dimensional (or N-dimensional) chart where each point represents a combination of values for the descriptors for a given conformation of a chemical. The application of the chemical selection tools to select chemicals to expand a preliminary model of human oestrogen receptor (hER) ligand binding to more adequately cover a diverse chemical inventory is presented to demonstrate the application of these tools.

The use of metabolising systems for in vitro testing of endocrine disruptors.

Legislation and prospective legislative proposals in for instance the USA, Europe, and Japan require, or may require that chemicals are tested for their ability to disrupt the hormonal systems of mammals. Chemicals found to test positive are considered to be endocrine active substances (EAS) and may be putative endocrine disruptors (EDs). To date, there is still little or no experience with incorporating metabolic and toxicokinetic aspects into in vitro tests for EAS. This is a situation in sharp contrast to genotoxicity testing, where in vitro tests are routinely conducted with and without metabolic capacity. Originally prepared for the Organisation of Economic Cooperation and Development (OECD), this detailed review paper reviews why in vitro assays for EAS should incorporate mammalian systems of metabolism and metabolic enzyme systems, and indicates how this could be done. The background to ED testing, the available test methods, and the role of mammalian metabolism in the activation and the inactivation of both endogenous and exogenous steroids are described. The available types of systems are compared, and the potential problems in incorporating systems in in vitro tests for EAS, and how these might be overcome, are discussed. Lastly, some recommendations for future activities are made.

Discriminating redox cycling and arylation pathways of reactive chemical toxicity in trout hepatocytes.

The toxicity of four quinones, 2,3-dimethoxy-1,4-naphthoquinone (DMONQ), 2-methyl-1,4-naphthoquinone (MNQ), 1,4-naphthoquinone (NQ), and 1,4-benzoquinone (BQ), which redox cycle or arlyate in mammalian cells, was determined in isolated trout (Oncorhynchus mykiss) hepatocytes. More than 70% of cells died in 3 h when exposed to BQ or NQ; 50% died in 7 h when exposed to MNQ, with no mortality compared to controls after 7 h DMONQ exposure. A suite of biochemical parameters was assessed for ability to discriminate these reactivity pathways in fish. Rapid depletion of glutathione (GSH) with appearance of glutathione disulfide (GSSG) and increased dichlorofluoroscein fluorescence were used as indicators of redox cycling, noted with DMONQ, MNQ, and NQ. Depletion of GSH with no GSSG accumulation, and loss of free protein thiol (PrSH) groups (nonreducible) indicated direct arylation by BQ. All toxicants rapidly oxidized NADH, with changes in NADPH noted later (BQ, NQ, MNQ) or not at all (DMONQ). Biochemical measures including cellular energy status, cytotoxicity, and measures of reactive oxygen species, along with the key parameters of GSH and PrSH redox status, allowed differentiation of responses associated with lethality. Chemicals that arylate were more potent than redox cyclers. Toxic pathway discrimination is needed to group chemicals for potency predictions and identification of structural parameters associated with distinct types of reactive toxicity, a necessary step for development of mechanistically based quantitative structure-activity relationships (QSARs) to predict chemical toxic potential. The commonality of reactivity mechanisms between rodents and fish was also demonstrated, a step essential for species extrapolations.

In silico modelling of hazard endpoints: current problems and perspectives.

Major scientific hurdles in the acceptance of quantitative structure-activity relationships (QSAR) for regulatory purposes have been identified. First, when quantifying important features of chemical structure complexities of molecular structure have often been ignored. More mechanistic modelling of chemical structure should proceed on two fronts: by developing a more in-depth understanding and representation of the multiple states possible for a single chemical by achieving greater rigor in understanding of conformational flexibility of chemicals; and, by considering families of activated metabolites that are derived in biological systems from an initial chemical substrate. Second, QSAR research is severely limited by the lack of systematic databases for important risk assessment endpoints, and despite many decades of research, the ability to cluster reactive chemicals by common toxicity pathways is in its infancy. Finally, computational tools are lacking for defining where a specific QSAR is applicable within the domain (universe) of chemical structures that are to be regulated. This paper describes some of the approaches being taken to address these needs. Applications of some of these new approaches are demonstrated for the prediction of chemical mutagenicity, where considerations of both molecular flexibility and metabolic activation improved the QSAR predictability and interpretations. Lastly, the applicability domain for a specific QSAR predicting estrogen receptor binding is presented in the context of a mechanistically-defined chemical structure space for large heterogeneous chemical datasets of regulatory concern. A strategic approach is discussed to selecting chemicals for model improvement and validation until regulatory acceptance criteria for risk assessment applications are met.

A reactivity pattern for discrimination of ER agonism and antagonism based on 3-D molecular attributes.

Various models have been developed to predict the relative binding affinity (RBA) of chemicals to estrogen receptors (ER). These models can be used to prioritize chemicals for further tiered biological testing to assess the potential for endocrine disruption. One shortcoming of models predicting RBA has been the inability to distinguish potential receptor antagonism from agonism, and hence in vivo response. It has been suggested that steroid receptor antagonists are less compact than agonists; thus, ER binding of antagonists may prohibit proper alignment of receptor protein helices preventing subsequent transactivation. The current study tests the theory of chemical bulk as a defining parameter of antagonism by employing a 3-D structural approach for development of reactivity patterns for ER antagonists and agonists. Using a dataset of 23 potent ER ligands (16 agonists, 7 antagonists), molecular parameters previously found to be associated with ER binding affinity, namely global (E(HOMO)) and local (donor delocalizabilities and charges) electron donating ability of electronegative sites and steric distances between those sites, were found insufficient to discriminate ER antagonists from agonists. However, parameters related to molecular bulk, including solvent accessible surface and negatively charged Van der Waal's surface, provided reactivity patterns that were 100% successful in discriminating antagonists from agonists in the limited data set tested. The model also shows potential to discriminate pure antagonists from partial agonist/antagonist structures. Using this exploratory model it is possible to predict additional chemicals for their ability to bind but inactivate the ER, providing a further tool for hypothesis testing to elucidate chemical structural characteristics associated with estrogenicity and anti-estrogenicity.

Reactivity profiles of ligands of mammalian retinoic acid receptors: a preliminary COREPA analysis.

Retinoic acid and associated derivatives comprise a class of endogenous hormones that bind to and activate different families of retinoic acid receptors (RARs, RXRs), and control many aspects of vertebrate development. Identification of potential RAR and RXR ligands is of interest both from a pharmaceutical and toxicological perspective. The recently developed COREPA (COmmon REactivity PAttern) algorithm was used to establish reactivity profiles for a limited data set of retinoid receptor ligands in terms of activation of three RARs (alpha, beta, gamma) and an RXR (alpha). Conformational analysis of a training set of retinoids and related analogues in terms of thermodynamic stability of conformers and rotational barriers showed that these chemicals tend to be quite flexible. This flexibility, and the observation that relatively small energy differences between conformers can result in significant variations in electronic structure, highlighted the necessity of considering all energetically reasonable conformers in defining common reactivity profiles. The derived reactivity patterns for three different subclasses of the RAR (alpha, beta, gamma) were similar in terms of their global electrophilicity (nucleophilicity) and steric parameters. However, the profile of active chemicals with respect to interaction with the RXR-alpha differed qualitatively from that of the RARs. Variations in reactivity profiles for the RAR versus RXR families would be consistent with established differences in their affinity for endogenous retinoids, likely reflecting functional differences in the receptors.

MUSIC and aromatic residues: amino acid type-selective (1)H-(15)N correlations, III.

Amino acid type-selective experiments can help to remove ambiguities in automated assignment procedures for (15)N/(13)C-labeled proteins. Here we present five triple-resonance experiments that yield amino acid type-selective (1)H-(15)N correlations for aromatic amino acids. Four of the novel experiments are based on the MUSIC coherence transfer scheme that replaces the initial INEPT transfer and is selective for CH(2). The MUSIC sequence is combined with selective excitation pulses to create experiments for Trp (W-HSQC) as well as Phe, Tyr, and His (FYH-HSQC). In addition, an experiment selective for Trp H(epsilon1)-N(epsilon1) is presented. The new experiments are recorded as two-dimensional experiments and their performance is demonstrated with the application to a protein domain of 115 amino acids.

The NMR structure of the 47-kDa dimeric enzyme 3,4-dihydroxy-2-butanone-4-phosphate synthase and ligand binding studies reveal the location of the active site.

Recent developments in NMR have extended the size range of proteins amenable to structural and functional characterization to include many larger proteins involved in important cellular processes. By applying a combination of residue-specific isotope labeling and protein deuteration strategies tailored to yield specific information, we were able to determine the solution structure and study structure-activity relationships of 3,4-dihydroxy-2-butanone-4-phosphate synthase, a 47-kDa enzyme from the Escherichia coli riboflavin biosynthesis pathway and an attractive target for novel antibiotics. Our investigations of the enzyme's ligand binding by NMR and site-directed mutagenesis yields a conclusive picture of the location and identity of residues directly involved in substrate binding and catalysis. Our studies illustrate the power of state-of-the-art NMR techniques for the structural characterization and investigation of ligand binding in protein complexes approaching the 50-kDa range in solution.

Amino acid type-selective backbone 1H-15N-correlations for Arg and Lys.

Four novel amino acid type-selective triple resonance experiments to identify the backbone amino proton and nitrogen resonances of Arg and Lys and of their sequential neighbors in (13C,15N)-labeled proteins are presented: the R(i+1)-HSQC and R(i,i+1)-HSQC select signals originating from Arg side chains, the K(i+1)-HSQC and K(i,i+1)-HSQC select signals originating from Lys side chains. The selection is based on exploiting the characteristic chemical shifts of a pair of carbon atoms in Arg and Lys side chains using selective 90 degree pulses. The new experiments are recorded as two-dimensional 1H-15N-correlations and their performance is demonstrated with the application to a protein domain of 83 amino acids.

MUSIC, selective pulses, and tuned delays: amino acid type-selective (1)H-(15)N correlations, II.

Amino acid type-selective experiments help to remove ambiguities in either manual or automated assignment procedures. Here we present modified triple-resonance experiments that yield amino acid type-selective (1)H-(15)N correlations. They are based on the MUSIC coherence transfer scheme which replaces the initial INEPT transfer and is selective for XH(2) or XH(3) (where X is either (15)N or (13)C). Signals of the desired amino acid types are thus selected based on the topology of the side chain. MUSIC is combined with selective pulses and carefully tuned delays to create experiments for Ser (S-HSQC); Val, Ile, and Ala (VIA-HSQC); Leu and Ala (LA-HSQC); Asp, Asn, and Gly (DNG-HSQC), as well as Glu, Gln, and Gly (EQG-HSQC). The new experiments are recorded as two-dimensional spectra and their performance is demonstrated by their application to two protein domains of 83 and 115 residues.

A computationally based identification algorithm for estrogen receptor ligands: part 1. Predicting hERalpha binding affinity.

The common reactivity pattern (COREPA) approach is a 3-dimensional, quantitative structure activity relationship (3-D QSAR) technique that permits identification and quantification of specific global and local stereoelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false positives and false negatives, and is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. The algorithm was recently used to screen chemical data sets for rat androgen receptor binding affinity. To further explore the potential application of the algorithm in establishing reactivity patterns for human estrogen receptor alpha (hERalpha) binding affinity, the stereoelectronic requirements associated with the binding affinity of 45 steroidal and nonsteroidal ligands to the receptor were defined. Reactivity patterns for relative hERalpha binding affinity (RBA; 17ss-estradiol = 100%) were established based on global nucleophilicity, interatomic distances between electronegative heteroatoms, and electron donor capability of heteroatoms. These reactivity patterns were used to establish descriptor profiles for identifying and ranking compounds with RBA of > 150%, 100-10%, 10-1%, and 1-0.1%. Increasing specificity of reactivity patterns was detected for ligand data sets with RBAs above 10%. Using the results of this analysis, an exploratory expert system was developed for use in ranking relative ER binding affinity potential for large chemical data sets.

A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha binding affinity model.

The objective of this study was to evaluate the capability of an expert system described in the previous paper (S. Bradbury et al., Toxicol. Sci. 58, 253-269) to identify the potential for chemicals to act as ligands of mammalian estrogen receptors (ERs). The basis of the expert system was a structure activity relationship (SAR) model, based on relative binding affinity (RBA) values for steroidal and nonsteroidal chemicals derived from human ERalpha (hERalpha) competitive binding assays. The expert system enables categorization of chemicals into (RBA ranges of < 0.1, 0.1 to 1, 1 to 10, 10 to 100, and >150% relative to 17ss-estradiol. In the current analysis, the algorithm was evaluated with respect to predicting RBAs of chemicals assayed with ERs from MCF7 cells, and mouse and rat uterine preparations. The best correspondence between predicted and observed RBA ranges was obtained with MCF7 cells. The agreement between predictions from the expert system and data from binding assays with mouse and rat ER(s) were less reliable, especially for chemicals with RBAs less than 10%. Prediction errors often were false positives, i.e., predictions of greater than observed RBA values. While discrepancies were likely due, in part, to species-specific variations in ER structure and ligand binding affinity, a systematic bias in structural characteristics of chemicals in the hERalpha training set, compared to the rodent evaluation data sets, also contributed to prediction errors. False-positive predictions were typically associated with ligands that had shielded electronegative sites. Ligands with these structural characteristics were not well represented in the training set used to derive the expert system. Inclusion of a shielding criterion into the original expert system significantly increased the accuracy of RBA predictions. With this additional structural requirement, 38 of 46 compounds with measured RBA values greater than 10% in hERalpha, MCF7, and rodent uterine preparations were correctly categorized. Of the remaining 129 compounds in the combined data sets, RBA values for 65 compounds were correctly predicted, with 47 of the incorrect predictions being false positives. Based upon this exploratory analysis, the modeling approach, combined with a high-quality training set of RBA values derived from a diverse set of chemical structures, could provide a credible tool for prioritizing chemicals with moderate to high ER binding affinity for subsequent in vitro or in vivo assessments.

Bridging the gap: A set of selective 1H-15N-correlations to link sequential neighbors of prolines.

Triple-resonance experiments are standard in the assignment of protein spectra. Conventional assignment strategies use 1H-15N-correlations as a starting point and therefore have problems when proline appears in the amino acid sequence, which lacks a signal in these correlations. Here we present a set of amino acid selective pulse sequences which provide the information to link the amino acid on either side of proline residues and thus complete the sequential assignment. The experiments yield amino acid type selective 1H-15N-correlations which contain signals from the amino protons of the residues either preceding or following proline in the amino acid sequence. These protons are correlated with their own nitrogen or with that of the proline. The new experiments are recorded as two-dimensional experiments and their performance is demonstrated by application to a 115-residue protein domain.

Dual epitope recognition by the VASP EVH1 domain modulates polyproline ligand specificity and binding affinity.

The Ena-VASP family of proteins act as molecular adaptors linking the cytoskeletal system to signal transduction pathways. Their N-terminal EVH1 domains use groups of exposed aromatic residues to specifically recognize 'FPPPP' motifs found in the mammalian zyxin and vinculin proteins, and ActA protein of the intracellular bacterium Listeria monocytogenes. Here, evidence is provided that the affinities of these EVH1-peptide interactions are strongly dependent on the recognition of residues flanking the core FPPPP motifs. Determination of the VASP EVH1 domain solution structure, together with peptide library screening, measurement of individual K(d)s by fluorescence titration, and NMR chemical shift mapping, revealed a second affinity-determining epitope present in all four ActA EVH1-binding motifs. The epitope was shown to interact with a complementary hydrophobic site on the EVH1 surface and to increase strongly the affinity of ActA for EVH1 domains. We propose that this epitope, which is absent in the sequences of the native EVH1-interaction partners zyxin and vinculin, may provide the pathogen with an advantage when competing for the recruitment of the host VASP and Mena proteins in the infected cell.

Depletion of cellular protein thiols as an indicator of arylation in isolated trout hepatocytes exposed to 1,4-benzoquinone.

A method to measure protein thiols (PrSH), reduced and oxidized, was adapted to determine PrSH depletion in isolated rainbow trout hepatocytes exposed to arylating agent 1,4-benzoquinone (BQ). Toxicant analysis revealed rapid conversion of BQ to 1, 4-hydroquinone (HQ) upon addition to hepatocytes. Hepatocytes exposed to 200 microM BQ+HQ showed 80% decline in glutathione (GSH) (1 h), 30% loss of PrSH (6 h), and no loss of viability (24 h). Recoverable oxidized PrSH was detected only after 24 h (200 microM BQ+HQ). Exposure to 600 microM BQ+HQ caused rapid (10 min) loss of > 90% GSH and > 60% PrSH, with eventual cell death. Half of the PrSH depletion at 6 h observed in hepatocytes exposed to 600 microM BQ+HQ was recoverable by reduction with dithiothreitol. Following the loss of GSH in hepatocytes exposed to 600 microM BQ+HQ, cellular PrSH were susceptible to direct arylation and oxidation. Rainbow trout hepatocytes, which contained 10-fold less GSH than rat cells, had a GSH:PrSH ratio of 1:82 compared with rat ratios of 1:2 to 1:6. The methods reported are useful for further study and discrimination of reactive modes of action needed for prediction of aquatic organism susceptibility to these types of toxicants.

Optimization of a precision-cut trout liver tissue slice assay as a screen for vitellogenin induction: comparison of slice incubation techniques.

An in vitro male rainbow trout liver slice assay has been developed for long-term incubation of precision-cut slices for the detection of vitellogenin (VTG) protein induction in response to xenobiotic chemicals. The assay was optimized to allow 72 h of incubation of slices to maximize detection of VTG, while maintaining slice viability. Two methods of incubation frequently used with rat liver slices were compared: (1) slices were submerged in media (11 degrees C) and cultured in 12-well plates (PL) with continuous shaking; or (2) slices were floated onto titanium screens, placed into glass vials, and held under dynamic organ culture (DOC) conditions (11 degrees C). Slices (200 µm) in modified L-15 media were exposed to 1.0 µM 17beta-estradiol (E2) or diethylstilbestrol (DES). Protein from media and slice was sampled for Western blot analysis, using a polyclonal antibody to detect appearance of VTG protein. Maximum VTG was seen at 72 h, with detectable protein at 24 and 48 h in slices and media following PL incubation. In contrast, slices incubated in DOC showed little detectable VTG above background levels after 72 h. This difference was not attributable to protein loss to vial or plate surfaces. Standard viability assays did not reveal any differences between slices incubated in PL or DOC. However, histopathological examination revealed earlier and more severe vacuolization in slices incubated in DOC. Significantly more E2 uptake and conversion to water-soluble metabolites was noted in PL, compared with DOC, as well as more production of VTG in response to DES and E2, correlated with less histologic change. The in vitro assay described allows tissue-level assessment of estrogenicity in aquatic organisms, and will be useful for assessing not only comparative species receptor binding and transactivation, but also the role of tissue-specific activation factors in the estrogenic response of fish.