Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches

Rapid Covalent-Probe Discovery by Electrophile-Fragment Screening

Covalent probes can display unmatched potency, selectivity, and duration of action; however, their discovery is challenging. In principle, fragments that can irreversibly bind their target can overcome the low affinity that limits reversible fragment screening, but such electrophilic fragments were considered nonselective and were rarely screened. We hypothesized that mild electrophiles might overcome the selectivity challenge and constructed a library of 993 mildly electrophilic fragments. We characterized this library by a new high-throughput thiol-reactivity assay and screened them against 10 cysteine-containing proteins. Highly reactive and promiscuous fragments were rare and could be easily eliminated. In contrast, we found hits for most targets. Combining our approach with high-throughput crystallography allowed rapid progression to potent and selective probes for two enzymes, the deubiquitinase OTUB2 and the pyrophosphatase NUDT7. No inhibitors were previously known for either. This study highlights the potential of electrophile-fragment screening as a practical and efficient tool for covalent-ligand discovery

Doctor of Philosophy

dissertationThe dysregulation of proteinâ€"protein interaction (PPI) networks has been implicated in many diseases. Designing therapeutic small-molecule inhibitors of these interactions is a challenging field for medicinal chemistry. This work advances the techniques for discovering more potent PPI inhibitors through integration of computational and biochemical techniques. High-throughput screening using fluorescence polarization and AlphaScreen assays identified an acyl hydrazone-containing inhibitor of the β-catenin/Tcf4 PPI, a key mediator of the canonical Wnt signaling pathway. By removing the undesirable acyl hydrazone moiety, a new compound, 4-(5H-[1,2,5]oxadiazolo[3',4':5,6]pyrazino[2,3-b]indol-5-yl)butanoic acid, was developed to selectively inhibit the β-catenin/Tcf4 interaction. The ethyl ester of this compound was tested in zebrafish embryos and shown to inhibit Wnt signaling in vivo at 2 and 10 μM concentrations. Differences between the PPI interface and the active site of traditional targets add to the difficulty of discovering PPI inhibitors. Herein, the relationship between inhibitor potency and ligand burialâ€"defined as the fraction of the solvent accessible surface areas of the bound over unbound ligand, θlâ€"in the PPI surface was evaluated. A positive correlation between θl and inhibitor potency was discovered. However, this correlation was secondary to the strong nonbonding interactions. A study of five PPI targets with corresponding inhibitor-bound crystal structures also revealed that empirical scoring functions were slightly better at identifying known inhibitors out of the putatively inactive test set, and the Lamarckian genetic algorithm was more successful at pose prediction. Due to the nature of the PPI surface, directly targeting the binding site may be difficult. A novel combination of computational methods explored the druggability, selectivity, and potential allosteric regulation of PPIs. Solvent mapping confirmed that Tcf4, E-cadherin, APC and axin use the same binding site on β-catenin in different ways. Evolutionary trace analysis indicated that the region surrounding W504 of β-catenin might be a potentially allosteric site. Site-directed mutagenesis testing results for a W504I β-catenin mutant resulted in three-fold increased binding of Tcf4 to β-catenin over the wild-type. This new site is promising for the discovery of future allosteric inhibitors of the β-catenin/Tcf4 PPI. The combined results from these studies reveals ways to better design PPI inhibitors

Analysis of Multitarget Activities and Assay Interference Characteristics of Pharmaceutically Relevant Compounds

The availability of large amounts of data in public repositories provide a useful source of knowledge in the field of drug discovery. Given the increasing sizes of compound databases and volumes of activity data, computational data mining can be used to study different characteristics and properties of compounds on a large scale. One of the major source of identification of new compounds in early phase of drug discovery is high-throughput screening where millions of compounds are tested against many targets. The screening data provides opportunities to assess activity profiles of compounds. This thesis aims at systematically mining activity data from publicly available sources in order to study the nature of growth of bioactive compounds, analyze multitarget activities and assay interference characteristics of pharmaceutically relevant compounds in context of polypharmacology. In the first study, growth of bioactive compounds against five major target families is monitored over time and compound-scaffold-CSK (cyclic skeleton) hierarchy is applied to investigate structural diversity of active compounds and topological diversity of their scaffolds. The next part of the thesis is based on the analysis of screening data. Initially, extensively assayed compounds are mined from the PubChem database and promiscuity of these compounds is assessed by taking assay frequencies into account. Next, DCM (dark chemical matter) or consistently inactive compounds that have been extensively tested are systematically extracted and their analog relationships with bioactive compounds are determined in order to derive target hypotheses for DCM. Further, PAINS (pan-assay interference compounds) are identified in the extensively tested set of compounds using substructure filters and their assay interference characteristics are studied. Finally, the limitations of PAINS filters are addressed using machine learning models that can distinguish between promiscuous and DCM PAINS. Structural context dependence of PAINS activities is studied by assessing predictions through feature weighting and mapping

High-Throughput Approaches for the Assessment of Factors Influencing Bioavailability of Small Molecules in Pre-Clinical Drug Development

A bioactive molecule must pass many hurdles to be designated as a good pharmaceutical lead or hit compound. It should have a significant activity, selectivity, bioavailability, and metabolic half-life. Many factors have been identified that influence the free drug concentration or bioavailability of orally administered drugs in the earliest development stages. In vitro pre-clinical assays have been developed to measure these parameters. The small molecule properties that are investigated here include aqueous solubility, permeability, reactivity (electrophilicity), small molecule-protein binding, and displacement of protein-bound molecules (drug-drug interactions). The development of rapid and miniaturized assays to quantify these factors is presented herein. First, a 384-well filter plate based assay was developed to determine the aqueous compound solubility to greatly decrease the time and amount of compound necessary for analysis. Secondly, one of the most common and simple permeability assays (parallel artificial membrane permeability assay, PAMPA) was optimized using a filter membrane impregnated with a long chain alkane (hexadecane) solution as an artificial membrane. Thirdly, permeability was also determined rapidly with the use of Immobilized Artificial Membrane (IAM) and C18 stationary phases by HPLC. The solitary and sequential usage of these columns was compared. Fourthly, a novel fluorescence-based high-throughput assay was developed to identify electrophilic molecules rapidly, in parallel, among small molecule libraries using only sub-milligram quantities. Subsequently, a filtration-based assay to estimate compound binding with plasma protein was developed for a 384-well plate format. This assay not only increases the throughput, but also addresses non-specific compound binding to the filtration apparatus, which is problematic with other ultrafiltration methods. Finally, a simple high-throughput competitive protein binding assay was developed based on the multiplexing of fluorescent small molecule probes with different spectroscopic and binding properties. The inhibition of probe-protein binding has been identified as a good indicator for plasma protein binding

IN SILICO SCREENING OF TASTE RECEPTORS: AN INTEGRATE MODELING APPROACH.

Taste is one of the five senses and accounts for the sensory impression of food or other substances on the tongue. It represents an innate mechanism of defence by which humans and animals detect safety or threat in food. Notably, taste is a whole-body experience since taste receptors, besides being located in the taste buds, are also found in non-sensory tissues, like the gut or the airways, playing still not completely known roles, for example, in glucose metabolism as well as in energy homeostasis. This clearly lays the groundwork for scientific investigations aimed to develop chemical tools through which modulate these physiopathological mechanisms. Although both GPCR and Ion Channels mediate these processes, this Thesis focuses on the latter class, so far less explored than the former one, involving four members of the Transient Potential Receptors family, namely TRPM8, TRPM5, TRPV1 and TRPV4. Although if each study presented its own objectives, peculiarities and relative computational approaches, a common path can be traced for all of them. First, the three-dimensional structure was generated by homology modelling techniques, by exploiting a well validated fragmental approach, then the obtained homology model was tested by docking calculations, which while including preliminary correlative studies, were always aimed at developing reliable strategies for virtual screening campaigns. The here reported results provide further remarkable confirmations for the reliability of the already modelled (and exploited) TRPM8 model, while the here generated TRPM5 and TRPV4 models afford results (despite obtained in a validating preliminary phase) in line with those of TRPM8 further emphasizing the reliability of the fragmental approach. Not to mention that the described targeted strategy to model TRPV1 suggests that previously generated homology models can be then exploited to assist the modeling of highly homologous proteins still obtaining encouraging results but with a significant saving of the required computational efforts. Finally, the here proposed TRPM8 results offer a convincing proof of the potential improvements that may be obtained combining ligand-based and structure-based approaches in a virtual screening analysis