Lestaurtinib Inhibits Histone Phosphorylation and Androgen-Dependent Gene Expression in Prostate Cancer Cells

Background: Epigenetics is defined as heritable changes in gene expression that are not based on changes in the DNA sequence. Posttranslational modification of histone proteins is a major mechanism of epigenetic regulation. The kinase PRK1 (protein kinase C related kinase 1, also known as PKN1) phosphorylates histone H3 at threonine 11 and is involved in the regulation of androgen receptor signalling. Thus, it has been identified as a novel drug target but little is known about PRK1 inhibitors and consequences of its inhibition. Methodology/Principal Finding: Using a focused library screening approach, we identified the clinical candidate lestaurtinib (also known as CEP-701) as a new inhibitor of PRK1. Based on a generated 3D model of the PRK1 kinase using the homolog PKC-theta (protein kinase c theta) protein as a template, the key interaction of lestaurtinib with PRK1 was analyzed by means of molecular docking studies. Furthermore, the effects on histone H3 threonine phosphorylation and androgen-dependent gene expression was evaluated in prostate cancer cells. Conclusions/Significance: Lestaurtinib inhibits PRK1 very potently in vitro and in vivo. Applied to cell culture it inhibits histone H3 threonine phosphorylation and androgen-dependent gene expression, a feature that has not been known yet. Thus our findings have implication both for understanding of the clinical activity of lestaurtinib as well as for future PRK

Application of Docking and QM/MM-GBSA Rescoring to Screen for Novel Myt1 Kinase Inhibitors

Identification of compounds that can bind to a target protein with high affinity is a nontrivial task in structure-based drug design. Several approaches ranging from simple scoring methods to more computationally demanding methods are usually applied for this purpose. In the current work, we used ligand docking in combination with QM/MM-GBSA, MM-GBSA, and MM-PBSA rescoring to discriminate between active and inactive Myt1 kinase inhibitors. Results show that QM/MM-GBSA rescoring performs better than normal docking scores or MM-GBSA rescoring in classifying active and inactive inhibitors. We also applied QM/MM-GBSA rescoring to estimate the binding affinities of compounds from different virtual screening runs. To prove our approach and to confirm its predictive power, a few compounds which were predicted to be active were purchased and experimentally tested. Among the five selected compounds, three showed significant inhibition of recombinant Myt1. PD-173952, which yielded a favorable QM/MM-GBSA binding free energy, showed a <i>K</i>_i value of 8.1 nM. In addition, two compounds, PD-180970 and saracatinib, showed inhibition at the low micromolar level. Thus, the developed protocol might be useful for further virtual screening experiments to better discriminate between active and inactive compounds and to further optimize the identified hits

Virtual Screening of PRK1 Inhibitors: Ensemble Docking, Rescoring Using Binding Free Energy Calculation and QSAR Model Development

Protein kinase C Related Kinase 1 (PRK1) has been shown to be involved in the regulation of androgen receptor signaling and has been identified as a novel potential drug target for prostate cancer therapy. Since there is no PRK1 crystal structure available to date, multiple PRK1 homology models were generated in order to address the protein flexibility. An in-house library of compounds tested on PRK1 was docked into the ATP binding site of the generated models. In most cases a correct pose of the inhibitors could be identified by ensemble docking, while there is still a challenge of finding a reasonable scoring function that is able to rank compounds according to their biological activity. We estimated the binding free energy for our data set of structurally diverse PRK1 inhibitors using the MM-PB­(GB)­SA and QM/MM-GBSA methods. The obtained results demonstrate that a correlation between calculated binding free energies and experimental IC₅₀ values was found to be usually higher than using docking scores. Furthermore, the developed approach was tested on a set of diverse PRK1 inhibitors taken from literature, which resulted in a significant correlation. The developed method is computationally inexpensive and can be applied as a postdocking filter in virtual screening as well as for optimization of PRK1 inhibitors in order to prioritize compounds for further biological characterization

Covalent inhibition of the histamine H₃ receptor

Covalent binding of G protein-coupled receptors by small molecules is a useful approach for better understanding of the structure and function of these proteins. We designed, synthesized and characterized a series of 6 potential covalent ligands for the histamine H3 receptor (H3R). Starting from a 2-amino-pyrimidine scaffold, optimization of anchor moiety and warhead followed by fine-tuning of the required reactivity via scaffold hopping resulted in the isothiocyanate H3R ligand 44. It shows high reactivity toward glutathione combined with appropriate stability in water and reacts selectively with the cysteine sidechain in a model nonapeptide equipped with nucleophilic residues. The covalent interaction of 44 with H3R was validated with washout experiments and leads to inverse agonism on H3R. Irreversible binder 44 (VUF15662) may serve as a useful tool compound to stabilize the inactive H3R conformation and to study the consequences of prolonged inhibition of the H3R

Known and newly identified PRK1 inhibitors.

<p>Known and newly identified PRK1 inhibitors.</p

PRK1 in vitro binding.

<p>PRK1 in vitro binding.</p

Effects of lestaurtinib on the mRNA expression of the androgen receptor target genes.

<p>Expression of androgen receptor target genes <i>TMPRSS2</i>, <i>IGF1-R</i>, <i>NKX3.1</i>, <i>CXCR4</i>, <i>MAK</i>, <i>MAF</i>, <i>N4A1</i>, <i>GREB1</i> and <i>FKBP5</i> measured by qRT-PCR in androgen (R1881) stimulated LNCaP prostate cancer cells are lowered by the treatment with lestaurtinib (final concentration 5 µM). The mRNA expression of the <i>GAPDH</i> gene was used as a control. Bars represent mean + SD (n = 5). P-value: ns  =  non significant; *  =  <0.05; **<0.01; ***<0.001.</p

Details of the binding of (A) staurosporine (green), (B) K252a (orange), (C) lestaurtinib (cyan), and (D) Ro318220 (dark-yellow) to the PRK1 kinase domain.

<p>Common interactions of the inhibitors are hydrogen bonds involving Glu696 and Ser698 of the hinge region, and van-der-Waals interactions with the gatekeeper residue Met695, as well as with Val629, Phe626, Leu747, and Phe904. In addition, some of the inhibitors interact with Asp744, Asn745 and a conserved water molecule (red sphere) nearby the Mg²⁺ binding site of the kinase. The backbone is shown as a purple ribbon. Only relevant amino acids are displayed. Hydrogen bonds are shown as dashed orange colored lines.</p

Vanishing white matter: Eukaryotic initiation factor 2B model and the impact of missense mutations

Abstract Background Vanishing white matter (VWM) is a leukodystrophy, caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B)‐subunit genes (EIF2B1–EIF2B5); 80% are missense mutations. Clinical severity is highly variable, with a strong, unexplained genotype–phenotype correlation. Materials and Methods With information from a recent natural history study, we severity‐graded 97 missense mutations. Using in silico modeling, we created a new human eIF2B model structure, onto which we mapped the missense mutations. Mutated residues were assessed for location in subunits, eIF2B complex, and functional domains, and for information on biochemical activity. Results Over 50% of mutations have (ultra‐)severe phenotypic effects. About 60% affect the ε‐subunit, containing the catalytic domain, mostly with (ultra‐)severe effects. About 55% affect subunit cores, with variable clinical severity. About 36% affect subunit interfaces, mostly with severe effects. Very few mutations occur on the external eIf2B surface, perhaps because they have minor functional effects and are tolerated. One external surface mutation affects eIF2B‐substrate interaction and is associated with ultra‐severe phenotype. Conclusion Mutations that lead to (ultra‐)severe disease mostly affect amino acids with pivotal roles in complex formation and function of eIF2B. Therapies for VWM are emerging and reliable mutation‐based phenotype prediction is required for propensity score matching for trials and in the future for individualized therapy decisions

Vanishing white matter: Eukaryotic initiation factor 2B model and the impact of missense mutations

Background: Vanishing white matter (VWM) is a leukodystrophy, caused by recessive mutations in eukaryotic initiation factor 2B (eIF2B)-subunit genes (EIF2B1–EIF2B5); 80% are missense mutations. Clinical severity is highly variable, with a strong, unexplained genotype–phenotype correlation. Materials and Methods: With information from a recent natural history study, we severity-graded 97 missense mutations. Using in silico modeling, we created a new human eIF2B model structure, onto which we mapped the missense mutations. Mutated residues were assessed for location in subunits, eIF2B complex, and functional domains, and for information on biochemical activity. Results: Over 50% of mutations have (ultra-)severe phenotypic effects. About 60% affect the ε-subunit, containing the catalytic domain, mostly with (ultra-)severe effects. About 55% affect subunit cores, with variable clinical severity. About 36% affect subunit interfaces, mostly with severe effects. Very few mutations occur on the external eIf2B surface, perhaps because they have minor functional effects and are tolerated. One external surface mutation affects eIF2B-substrate interaction and is associated with ultra-severe phenotype. Conclusion: Mutations that lead to (ultra-)severe disease mostly affect amino acids with pivotal roles in complex formation and function of eIF2B. Therapies for VWM are emerging and reliable mutation-based phenotype prediction is required for propensity score matching for trials and in the future for individualized therapy decisions