PhotoAffinity bits : a photoaffinity-based fragment screening platform for efficient identification of protein ligands

Advances in genomic analyses enable the identification of new proteins that are associated with disease. To validate these targets, tool molecules are required to demonstrate that a ligand can have a disease-modifying effect. Currently, as tools are reported for only a fraction of the proteome, platforms for ligand discovery are essential to leverage insights from genomic analyses. Fragment screening offers an efficient approach to explore chemical space, however, it remains challenging to develop techniques that are both sufficiently high-throughput and sensitive. We present a fragment screening platform, termed PhABits (PhotoAffinity Bits), which utilises a library of photoreactive fragments to covalently capture fragment-protein interactions. Hits can be profiled to determine potency and site of crosslinking, and subsequently developed as reporters in a competitive displacement assay to identify novel hit matter. We envision that the PhABits will be widely applicable to novel protein targets, identifying starting points in the development of therapeutic

The discovery of I-BRD9, a selective cell active chemical probe for bromodomain containing protein 9 inhibition

Acetylation of histone lysine residues is one of the most well-studied post-translational modifications of chromatin, selectively recognized by bromodomain “reader” modules. Inhibitors of the bromodomain and extra terminal domain (BET) family of bromodomains have shown profound anticancer and anti-inflammatory properties, generating much interest in targeting other bromodomain-containing proteins for disease treatment. Herein, we report the discovery of I-BRD9, the first selective cellular chemical probe for bromodomain-containing protein 9 (BRD9). I-BRD9 was identified through structure-based design, leading to greater than 700-fold selectivity over the BET family and 200-fold over the highly homologous bromodomain-containing protein 7 (BRD7). I-BRD9 was used to identify genes regulated by BRD9 in Kasumi-1 cells involved in oncology and immune response pathways and to the best of our knowledge, represents the first selective tool compound available to elucidate the cellular phenotype of BRD9 bromodomain inhibition

A theoretical entropy score as a single value to express inhibitor selectivity

<p>Abstract</p> <p>Background</p> <p>Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.</p> <p>Results</p> <p>Here we propose a new theoretical entropy score that can be calculated from a set of IC₅₀data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC₅₀data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.</p> <p>Conclusions</p> <p>For quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.</p

A Cell-Based Small Molecule Screening Method for Identifying Inhibitors of Epithelial-Mesenchymal Transition in Carcinoma

Epithelial Mesenchymal Transition (EMT) is a crucial mechanism for carcinoma progression, as it provides routes for in situ carcinoma cells to dissociate and become motile, leading to localized invasion and metastatic spread. Targeting EMT therefore represents an important therapeutic strategy for cancer treatment. The discovery of oncogene addiction in sustaining tumor growth has led to the rapid development of targeted therapeutics. Whilst initially optimized as anti-proliferative agents, it is likely that some of these compounds may inhibit EMT initiation or sustenance, since EMT is also modulated by similar signaling pathways that these compounds were designed to target. We have developed a novel screening assay that can lead to the identification of compounds that can inhibit EMT initiated by growth factor signaling. This assay is designed as a high-content screening assay where both cell growth and cell migration can be analyzed simultaneously via time-course imaging in multi-well plates. Using this assay, we have validated several compounds as viable EMT inhibitors. In particular, we have identified compounds targeting ALK5, MEK, and SRC as potent inhibitors that can interfere with EGF, HGF, and IGF-1 induced EMT signaling. Overall, this EMT screening method provides a foundation for improving the therapeutic value of recently developed compounds in advanced stage carcinoma

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

<p>Abstract</p> <p>Background</p> <p>Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.</p> <p>Results</p> <p>We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (K_d). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P²= 0.67-0.73; for new kinases it ranged P²_kin= 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P²= 0.47, P²_kin= 0.42 and AUC = 0.83.</p> <p>Conclusions</p> <p>Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.</p

Kinase inhibitors for the treatment of inflammatory and autoimmune disorders

Drugs targeting inhibition of kinases for the treatment of inflammation and autoimmune disorders have become a major focus in the pharmaceutical and biotech industry. Multiple kinases from different pathways have been the targets of interest in this endeavor. This review describes some of the recent developments in the search for inhibitors of IKK2, Syk, Lck, and JAK3 kinases. It is anticipated that some of these compounds or newer inhibitors of these kinases will be approved for the treatment of rheumatoid arthritis, psoriasis, organ transplantation, and other autoimmune diseases

Epigenetic polypharmacology: from combination therapy to multitargeted drugs

The modern drug discovery process has largely focused its attention in the so-called magic bullets, single chemical entities that exhibit high selectivity and potency for a particular target. This approach was based on the assumption that the deregulation of a protein was causally linked to a disease state, and the pharmacological intervention through inhibition of the deregulated target was able to restore normal cell function. However, the use of cocktails or multicomponent drugs to address several targets simultaneously is also popular to treat multifactorial diseases such as cancer and neurological disorders. We review the state of the art with such combinations that have an epigenetic target as one of their mechanisms of action. Epigenetic drug discovery is a rapidly advancing field, and drugs targeting epigenetic enzymes are in the clinic for the treatment of hematological cancers. Approved and experimental epigenetic drugs are undergoing clinical trials in combination with other therapeutic agents via fused or linked pharmacophores in order to benefit from synergistic effects of polypharmacology. In addition, ligands are being discovered which, as single chemical entities, are able to modulate multiple epigenetic targets simultaneously (multitarget epigenetic drugs). These multiple ligands should in principle have a lower risk of drug-drug interactions and drug resistance compared to cocktails or multicomponent drugs. This new generation may rival the so-called magic bullets in the treatment of diseases that arise as a consequence of the deregulation of multiple signaling pathways provided the challenge of optimization of the activities shown by the pharmacophores with the different targets is addressed

Comprehensive characterization of the Published Kinase Inhibitor Set

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein-coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome

Structure-based design of a bromodomain and extraterminal domain (BET) inhibitor selective for the N-terminal bromodomains that retains an anti-inflammatory and antiproliferative phenotype

The bromodomain and extraterminal domain (BET) family of epigenetic regulators comprises four proteins (BRD2, BRD3, BRD4, BRDT), each containing tandem bromodomains. To date, small molecule inhibitors of these proteins typically bind all eight bromodomains of the family with similar affinity, resulting in a diverse range of biological effects. To enable further understanding of the broad phenotype characteristic of pan-BET inhibition, the development of inhibitors selective for individual, or sets of, bromodomains within the family is required. In this regard, we report the discovery of a potent probe molecule possessing up to 150-fold selectivity for the N-terminal bromodomains (BD1s) over the C-terminal bromodomains (BD2s) of the BETs. Guided by structural information, a specific amino acid difference between BD1 and BD2 domains was targeted for selective interaction with chemical functionality appended to the previously developed I-BET151 scaffold. Data presented herein demonstrate that selective inhibition of BD1 domains is sufficient to drive anti-inflammatory and antiproliferative effects