Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein-ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein-ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors.

Assessment of Bromodomain Target Engagement by a Series of BI2536 Analogues with Miniaturized BET-BRET

Evaluating the engagement of a small molecule ligand with a protein target in cells provides useful information for chemical probe optimization and pharmaceutical development. While several techniques exist that can be performed in a low-throughput manner, systematic evaluation of large compound libraries remains a challenge. In-cell engagement measurements are especially useful when evaluating compound classes suspected to target multiple cellular factors. In this study we used a bioluminescent resonant energy transfer assay to assess bromodomain engagement by a compound series containing bromodomain- and kinase-biasing polypharmacophores based on the known dual BRD4 bromodomain/PLK1 kinase inhibitor BI2536. With this assay, we discovered several novel agents with bromodomain-selective specificity profiles and cellular activity. Thus, this platform aids in distinguishing molecules whose cellular activity is difficult to assess due to polypharmacologic effects.

Abstract 3643: Targeting the acetyl-lysine binding site of BRD4 with dual nanomolar BET-JAK2 inhibitors: A new anticancer therapeutic strategy

Bromodomain (BRD)-containing proteins are essential for the recognition of acetylated lysine residues of histones during transcriptional activation. The BRD-containing proteins have emerged as promising drug targets for a number of diseases, including many cancers, that are characterized by changes in the epigenetic cell signature. Recent reports have shown that targeting BRD4 with small molecules may represent a new way to treat prostate and breast cancer, acute myeloid leukemia and melanoma. We have identified many type 1 and type 2 kinase inhibitors which also inhibit BRD4 by robotic co-crystallization screening of kinase inhibitor libraries against BRD4. In each case the co-crystal structure unambiguously revealed the inhibitor bound to the acetyl lysine site of BRD4-1. The identified BRD4 ligands were subjected to differential scanning fluorimetry (DSF) and AlphaScreen assay to assess their binding and inhibitory potentials against BRD4. As shown previously for other BRD-inhibitor-protein complexes, the melting temperatures of BRD4-kinase inhibitor complexes were logarithmically proportional to their IC50 values. We now report the design, synthesis, structural analysis and biological evaluation of next-generation nanomolar BET-selective and nanomolar dual-activity BET-JAK2 inhibitors, based on the initial co-crystallization screening hits. Structure activity relationships were developed using both DSF and co-crystallization of the ligands with BRD4, to assess binding potential and binding modes, respectively. We report initial evaluation of the anticancer potential of compounds possessing dual potent BRD4 and JAK2 inhibitory properties. In addition to myeloma cell lines, this includes the evaluation of dual BRD4-JAK2 inhibitor compounds against JAK2-driven myeloproliferative neoplasm cell lines and primary cells from patients.

Citation Format: Steven Gunawan, Ayaz Muhammad, Stuart W. J. Ember, Jin-Yi Zhu, Rebecca A. Jacobsen, Norbert Berndt, Que T. Lambert, Gary W. Reuther, Harshani R. Lawrence, Ernst Schonbrunn, Nicholas J. Lawrence. Targeting the acetyl-lysine binding site of BRD4 with dual nanomolar BET-JAK2 inhibitors: A new anticancer therapeutic strategy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3643. doi:10.1158/1538-7445.AM2015-3643

Fluorinated aromatic amino acids are sensitive 19F NMR probes for bromodomain-ligand interactions

We describe a ¹⁹F NMR method for detecting bromodomain–ligand interactions using fluorine-labeled aromatic amino acids due to the conservation of aromatic residues in the bromodomain binding site. We test the sensitivity, accuracy, and speed of this method with small molecule ligands (+)-JQ1, BI2536, Dinaciclib, TG101348, and acetaminophen using three bromodomains Brd4, BrdT, and BPTF. Simplified ¹⁹F NMR spectra allowed for simultaneous testing of multiple bromodomains to assess selectivity and identification of a new BPTF ligand. Fluorine labeling only modestly affected the Brd4 structure and function assessed by isothermal titration calorimetry, circular dichroism, and X-ray crystallography. The speed, ease of interpretation, and low concentration of protein needed for binding experiments affords a new method to discover and characterize both native and new ligands.

Acetyl-lysine binding site of bromodomain-containing protein 4 (BRD4) interacts with diverse kinase inhibitors

Members of the bromodomain and extra terminal (BET) family of proteins are essential for the recognition of acetylated lysine (KAc) residues in histones and have emerged as promising drug targets in cancer, inflammation, and contraception research. In co-crystallization screening campaigns using the first bromodomain of BRD4 (BRD4-1) against kinase inhibitor libraries, we identified and characterized 14 kinase inhibitors (10 distinct chemical scaffolds) as ligands of the KAc binding site. Among these, the PLK1 inhibitor BI2536 and the JAK2 inhibitor TG101209 displayed strongest inhibitory potential against BRD4 (IC₅₀ = 25 nM and 130 nM, respectively) and high selectivity for BET bromodomains. Comparative structural analysis revealed markedly different binding modes of kinase hinge-binding scaffolds in the KAc binding site, suggesting that BET proteins are potential off-targets of diverse kinase inhibitors. Combined, these findings provide a new structural framework for the rational design of next-generation BET-selective and dual-activity BET-kinase inhibitors.

Vaccinia virus immune evasion: mechanisms, virulence and immunogenicity

Virus infection of mammalian cells is sensed by pattern recognition receptors and leads to an innate immune response that restricts virus replication and induces adaptive immunity. In response, viruses have evolved many countermeasures that enable them to replicate and be transmitted to new hosts, despite the host innate immune response. Poxviruses, such as vaccinia virus (VACV), have large DNA genomes and encode many proteins that are dedicated to host immune evasion. Some of these proteins are secreted from the infected cell, where they bind and neutralize complement factors, interferons, cytokines and chemokines. Other VACV proteins function inside cells to inhibit apoptosis or signalling pathways that lead to the production of interferons and pro-inflammatory cytokines and chemokines. In this review, these VACV immunomodulatory proteins are described and the potential to create more immunogenic VACV strains by manipulation of the gene encoding these proteins is discussed.

Vaccinia virus protein C4 inhibits NF-κB activation and promotes virus virulence

Vaccinia virus (VACV) strain Western Reserve protein C4 has been characterized and its function and contribution to virus virulence assessed. Bioinformatic analysis showed that C4 is conserved in six orthopoxvirus species and shares 43 % amino acid identity with VACV protein C16, a known virulence factor. A recombinant VACV expressing a C-terminally tagged version of C4 showed that, like C16, this 37 kDa protein is expressed early during infection and localizes to both the cytoplasm and the nucleus. Functional assays using a firefly luciferase reporter plasmid under the control of a nuclear factor kappa B (NF-κB)-dependent promoter demonstrated that C4 inhibits NF-κB activation at, or downstream of, the inhibitor of kappa kinase (IKK) complex. Consistent with this, C4 inhibited interleukin-1β-induced translocation of p65 into the nucleus. A VACV lacking the C4L gene (vΔC4) showed no significant differences from wild-type virus in growth kinetics or spread in cell culture, but had reduced virulence in a murine intranasal model of infection. vΔC4-infected mice exhibited fewer symptoms, lost less weight and recovered 7 days earlier than animals infected with control viruses expressing C4. Furthermore, bronchoalveolar lavage fluid from vΔC4-infected mice had increased cell numbers at day 5 post-infection, which correlated with reduced lung virus titres from this time onward. C4 represents the ninth VACV protein to inhibit NF-κB activation and remarkably, in every case examined, loss of each protein individually caused an alteration in virus virulence, despite the presence of other NF-κB inhibitors.

Peptide-tags for enhanced DNA microarray performance

DNA microarrays are powerful tools for gene expression analysis and genotyping studies in research and diagnostic applications. A high sensitivity and short time-to-result are prerequisites for their practical application in the clinic. The hybridization efficiency of DNA microarrays depends on the probe density and the probe orientation and thus their accessibility for target molecules. In order to find an optimal probe immobilization procedure a set of different oligonucleotide modifications was tested on epoxy silane functionalized glass slides. It was found that histidine-tagged oligonucleotides resulted in the highest amount of bound probe and by far the best hybridization efficiencies. The detection limit obtained with histidine-tagged probes was up to two orders of magnitude lower compared to commonly used probe modifications. In order to further investigate the binding mechanism of histidine-tags towards functionalized glass substrates a set of different peptide-tags with and without free terminal amino-groups and with different amino acid compositions was tested. The results indicate an impact of the terminal amino group on the covalent surface binding and of aromatic amino acid residues on the enhanced hybridisation efficiency.

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).