Estrogen receptor alpha (ERα) is involved in numerous physiological and pathological processes, including breast cancer. Breast cancer therapy is therefore currently directed at inhibiting the transcriptional potency of ERα, either by blocking estrogen production through aromatase inhibitors or antiestrogens that compete for hormone binding. Due to resistance, new treatment modalities are needed and as ERα dimerization is essential for its activity, interference with receptor dimerization offers a new opportunity to exploit in drug design. Here we describe a unique mechanism of how ERα dimerization is negatively controlled by interaction with 14-3-3 proteins at the extreme C terminus of the receptor. Moreover, the small-molecule fusicoccin (FC) stabilizes this ERα/14-3-3 interaction. Cocrystallization of the trimeric ERα/ 14-3-3/FC complex provides the structural basis for this stabilization and shows the importance of phosphorylation of the penultimate Threonine (ERα-T 594 ) for high-affinity interaction. We confirm that T 594 is a distinct ERα phosphorylation site in the breast cancer cell line MCF-7 using a phospho-T 594 -specific antibody and by mass spectrometry. In line with its ERα/14-3-3 interaction stabilizing effect, fusicoccin reduces the estradiol-stimulated ERα dimerization, inhibits ERα/chromatin interactions and downstream gene expression, resulting in decreased cell proliferation. Herewith, a unique functional phosphosite and an alternative regulation mechanism of ERα are provided, together with a small molecule that selectively targets this ERα/14-3-3 interface.T he estrogen receptor alpha (ERα) is a ligand-dependent transcription factor and the driving force of cell proliferation in 75% of all breast cancers. Current therapeutic strategies to treat these tumors rely on selective ER modulators (SERMs), like tamoxifen (TAM) (1) or aromatase inhibitors (AIs) that block estradiol synthesis (2). Although the benefits of treating hormone-sensitive breast cancers with SERMs and AIs are evident, resistance to treatment is commonly observed (3, 4). To overcome resistance, selective ERα down-regulators (SERDs) can for instance be applied that inhibit ERα signaling through receptor degradation (5, 6). Approaches that target the ERα/ DNA or ERα/cofactor interactions are explored as well (5, 7), but other essential steps in the ERα activation cascade are currently unexploited in drug design, also due to a lack of molecular understanding of the processes at hand.One such step that is crucial for many aspects of ERα functioning is ligand-driven receptor dimerization (8, 9). 17β-Estradiol (E2) association with the ERα ligand binding domain (LBD) drives large conformational changes (10) resulting in ERα dissociation from chaperones (11, 12), unmasking of domains for receptor dimerization, and DNA binding (13,14). Whereas the LBD contains the main dimerization domain (15), the extreme C-terminal domain of the receptor (F domain) imposes a restraint on dimerization (15, 16), although the regulation of this remain...

Molecular dynamics (MD) simulations and polarized subnanosecond time-resolved flavin fluorescence spectroscopy have been used to study the conformational dynamics of the flavin adenine dinucleotide (FAD) cofactor in aqueous solution. FAD displays a highly heterogeneous fluorescence intensity decay, resulting in lifetime spectra with two major components: a dominant 7-ps contribution that is characteristic of ultrafast fluorescence quenching and a 2.7-ns contribution resulting from moderate quenching. MD simulations were performed in both the ground state and first excited state. The simulations showed transitions from "open" conformations to "closed" conformations in which the flavin and adenine ring systems stack coplanarly. Stacking generally occurred within the lifetime of the flavin excited state (4.7 ns in water), and yielded a simulated fluorescence lifetime on the order of the nanosecond lifetime that was observed experimentally. Hydrogen bonds in the ribityl-pyrophosphate-ribofuranosyl chain connecting both ring systems form highly stable cooperative networks and dominate the conformational transitions of the molecule. Fluorescence quenching in FAD is mainly determined by the coplanar stacking of the flavin and adenine ring systems, most likely through a mechanism of photoinduced electron transfer. Whereas in stacked conformations fluorescence is quenched nearly instantaneously, open fluorescent conformations can stack during the lifetime of the flavin excited state, resulting in immediate fluorescence quenching upon stacking.

Table 1. Overview of CYP Pharmacophore and 3D-QSAR Models sets method a training b test c predicted property key features of pharmacophore ref CYP1A2 Combine and GRID/GOLPE + homology model na mutagenicity Inhibitor model includes H-bonding and hydrophobic binding sites (on the protein). Several residues in the homology model are found to interact with the pharmacophore model. 143 CYP2A5 CoMFA na IC50 Substrates and inhibitors have a negative electrostatic potential close to a lactone moiety, steric effects around a methoxy group on methoxasalen, and positive electrostatic potential para to a methoxy group. 171 PLS MS-WHIM na Ki Potent inhibitors have a positive molecular electrostatic potential and a H-bond acceptor. 172 CYP2A6 CoMFA and GRID/GOLPE 5 (ss) IC50 Potent CYP2A6 inhibitors do not include a lactone moiety. 145 CYP2B6 Catalyst and PLS MS-WHIM na (ss) Km, CSP Substrate model includes at least three hydrophobic regions 3.1, 4.6, and 5.3 Å from a H-bond acceptor. 173 Catalyst + homology model 5 (4) Km, CSP Substrate model includes two hydrophobic regions and one H-bond acceptor. Substrate catalytic site is 4.0 and 3.4 Å from two hydrophobic regions and 4.6 Å from a H-bond acceptor in model A and is 4.9, 4.1, and 7.8 Å, in model B. 116 CYP2C8/9/18/19 GRID/CPCA + homology model + docking na na CSP Combined protein-based inverse substrate model includes two hydrophobic and two electropositive binding sites (on the protein). 62 CYP2C8 Catalyst + homology model + docking na CSP Substrate model with H-bond acceptor and a hydrophobic and a cationic region. 117 CYP2C9 manual superposition na CSP Substrate model protein H-bond donor is 7 Å from substrate catalytic site. 174 manual superposition na CSP Substrate model includes anionic site is 7.8 Å from catalytic site, between hydrophobic regions. 36, 175CoMFA + homology model 14 ( 13) Ki, CSP Inhibitor model includes two cationic binding sites, along with an aromatic binding region and a steric region (on the protein). Substrates possess a partial negative charge at 10 Å and an anionic site at 6 Å from the catalytic site. 35 59 Catalyst and PLS-WHIM 14 (10) 14 (12)

Supplementary data are available at Bioinformatics online.

Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes.Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as ‘stepping stones’ for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or ‘trigger’ is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells.Contact: j.heringa@vu.nl or ioannis.xenarios@isb-sib.ch or bg200@cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.