Discovery and Characterization of the Potent and Highly Selective 1,7-Naphthyridine-Based Inhibitors BAY-091 and BAY-297 of the Kinase PIP4K2A.

PIP4K2A is an insufficiently studied type II lipid kinase that catalyzes the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). The involvement of PIP4K2A/B in cancer has been suggested, particularly in the context of p53 mutant/null tumors. PIP4K2A/B depletion has been shown to induce tumor growth inhibition, possibly due to hyperactivation of AKT and reactive oxygen species-mediated apoptosis. Herein, we report the identification of the novel potent and highly selective inhibitors BAY-091 and BAY-297 of the kinase PIP4K2A by high-throughput screening and subsequent structure-based optimization. Cellular target engagement of BAY-091 and BAY-297 was demonstrated using cellular thermal shift assay technology. However, inhibition of PIP4K2A with BAY-091 or BAY-297 did not translate into the hypothesized mode of action and antiproliferative activity in p53-deficient tumor cells. Therefore, BAY-091 and BAY-297 serve as valuable chemical probes to study PIP4K2A signaling and its involvement in pathophysiological conditions such as cancer.

Characterization of the Menin-MLL Interaction as Therapeutic Cancer Target.

Inhibiting the interaction of menin with the histone methyltransferase MLL1 (KMT2A) has recently emerged as a novel therapeutic strategy. Beneficial therapeutic effects have been postulated in leukemia, prostate, breast, liver and in synovial sarcoma models. In those indications, MLL1 recruitment by menin was described to critically regulate the expression of disease associated genes. However, most findings so far rely on single study reports. Here we independently evaluated the pathogenic functions of the menin-MLL interaction in a large set of different cancer models with a potent and selective probe inhibitor BAY-155. We characterized the inhibition of the menin-MLL interaction for anti-proliferation, gene transcription effects, and for efficacy in several in vivo xenografted tumor models. We found a specific therapeutic activity of BAY-155 primarily in AML/ALL models. In solid tumors, we observed anti-proliferative effects of BAY-155 in a surprisingly limited fraction of cell line models. These findings were further validated in vivo. Overall, our study using a novel, highly selective and potent inhibitor, shows that the menin-MLL interaction is not essential for the survival of most solid cancer models. We can confirm that disrupting the menin-MLL complex has a selective therapeutic benefit in MLL-fused leukemia. In solid cancers, effects are restricted to single models and more limited than previously claimed.

Isoform-Selective ATAD2 Chemical Probe with Novel Chemical Structure and Unusual Mode of Action.

ATAD2 (ANCCA) is an epigenetic regulator and transcriptional cofactor, whose overexpression has been linked to the progress of various cancer types. Here, we report a DNA-encoded library screen leading to the discovery of BAY-850, a potent and isoform selective inhibitor that specifically induces ATAD2 bromodomain dimerization and prevents interactions with acetylated histones in vitro, as well as with chromatin in cells. These features qualify BAY-850 as a chemical probe to explore ATAD2 biology.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors.

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.

Discovery and Characterization of a Highly Potent and Selective Aminopyrazoline-Based in Vivo Probe (BAY-598) for the Protein Lysine Methyltransferase SMYD2.

Protein lysine methyltransferases have recently emerged as a new target class for the development of inhibitors that modulate gene transcription or signaling pathways. SET and MYND domain containing protein 2 (SMYD2) is a catalytic SET domain containing methyltransferase reported to monomethylate lysine residues on histone and nonhistone proteins. Although several studies have uncovered an important role of SMYD2 in promoting cancer by protein methylation, the biology of SMYD2 is far from being fully understood. Utilization of highly potent and selective chemical probes for target validation has emerged as a concept which circumvents possible limitations of knockdown experiments and, in particular, could result in an improved exploration of drug targets with a complex underlying biology. Here, we report the development of a potent, selective, and cell-active, substrate-competitive inhibitor of SMYD2, which is the first reported inhibitor suitable for in vivo target validation studies in rodents.

Prediction of ligand binding affinity and orientation of xenoestrogens to the estrogen receptor by molecular dynamics simulations and the linear interaction energy method.

Exposure to environmental estrogens has been proposed as a risk factor for disruption of reproductive development and tumorigenesis of humans and wildlife (McLachlan, J. A.; Korach, K. S.; Newbold, R. R.; Degen, G. H. Diethylstilbestrol and other estrogens in the environment. Fundam. Appl. Toxicol. 1984, 4, 686-691). In recent years, many structurally diverse environmental compounds have been identified as estrogens. A reliable computational method for determining estrogen receptor (ER) binding affinity is of great value for the prediction of estrogenic activity of such compounds and their metabolites. In the presented study, a computational model was developed for prediction of binding affinities of ligands to the ERalpha isoform, using MD simulations in combination with the linear interaction energy (LIE) approach. The linear interaction energy approximation was first described by Aqvist et al. (Aqvist, J.; Medina, C.; Samuelsson, J. E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. 1994, 7, 385-391) and relies on the assumption that the binding free energy (DeltaG) depends linearly on changes in the van der Waals and electrostatic energy of the system. In the present study, MD simulations of ligands in the ERalpha ligand binding domain (LBD) (Shiau, A. K.; Barstad, D.; Loria, P. M.; Cheng, L.; Kushner, P. J.; Agard, D. A.; Greene, G. L. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 1998, 95, 927-937), as well as ligands free in water, were carried out using the Amber 6.0 force field (http://amber.scripps.edu/). Contrary to previous LIE methods, we took into account every possible orientation of the ligands in the LBD and weighted the contribution of each orientation to the total binding affinity according to a Boltzman distribution. The training set (n = 19) contained estradiol (E2), the synthetic estrogens diethylstilbestrol (DES) and 11beta-chloroethylestradiol (E2-Cl), 16alpha-hydroxy-E2 (estriol, EST), the phytoestrogens genistein (GEN), 8-prenylnaringenin (8PN), and zearalenon (ZEA), four derivatives of benz[a]antracene-3,9-diol, and eight estrogenic monohydroxylated PAH metabolites. We obtained an excellent linear correlation (r(2) = 0.94) between experimental (competitive ER binding assay) and calculated binding energies, with K(d) values ranging from 0.15 mM to 30 pM, a 5 000 000-fold difference in binding affinity. Subsequently, a test set (n = 12) was used to examine the predictive value of our model. This set consisted of the synthetic estrogen 5,11-cis-diethyl-5,6,11,12-tetrahydrochrysene-2,8-diol (THC), daidzein (DAI), equol (EQU) and apigenin (API), chlordecone (KEP), progesterone (PRG), several mono- and dihydroxylated PAH metabolites, and two brominated biphenyls. The predicted binding affinities of these estrogenic compounds were in very good agreement with the experimental values (average deviation of 0.61 +/- 0.4 kcal/mol). In conclusion, our LIE model provides a very good method for prediction of absolute ligand binding affinities, as well as binding orientation of ligands.

Modeling and molecular dynamics of glutamine transaminase K/cysteine conjugate beta-lyase.

The homodimeric, pyridoxal 5'-phosphate (PLP)-dependent enzyme glutamine transaminase K/cysteine conjugate beta-lyase (GTK/beta-lyase) has been implicated in the bioactivation of chemopreventive compounds. This paper describes the first homology model of rat renal GTK/beta-lyase and its active site residues, deduced from molecular dynamics (MD) simulations of the binding mode of 13 structurally diverse cysteine S-conjugates and amino acids after Amber-parametrization of PLP. Comparison with Thermus thermophilus aspartate aminotransferase (tAAT) and Trypanosoma cruzi tyrosine aminotransferase (tTAT), used as templates for modeling GTK/beta-lyase, showed that the PLP-binding site of GTK/beta-lyase is highly conserved. Binding of the ligand alpha-carboxylate-group occurred via the conserved residues Arg(432) and Asn(219), and Asn(50) and Gly(70). Two pockets accommodated the various ligand side chains. A small pocket, located directly above PLP, was of a highly hydrophobic and aromatic character. A larger pocket, formed partly by the substrate access channel, was more hydrophilic and notably involved the salt bridge partners Glu(54) and Arg(99*) (* denotes the other subunit). Ligand-binding residues included Leu(51), Phe(71), Tyr(135), Phe(373) and Phe(312*), and pi-stacking interactions were often observed. Tyr(135) and Asn(50) were prominent in hydrogen bonding with the sulfur-atom of cysteine S-conjugates. The observed binding mode of the ligands corresponded well with their experimentally determined inhibitory potency toward GTK/beta-lyase. The current homology model thus provides a starting point for further validation of the role of active site residues in ligand-binding by means of mutagenesis studies. Ultimately, insight in the binding of ligands to GTK/beta-lyase may result in the rational design of new ligands and selective inhibitors.