Aromatase inhibitors and antiepileptic drugs: a computational systems biology analysis

<p>Abstract</p> <p>Background</p> <p>The present study compares antiepileptic drugs and aromatase (CYP19) inhibitors for chemical and structural similarity. Human aromatase is well known as an important pharmacological target in anti-breast cancer therapy, but recent research demonstrates its role in epileptic seizures, as well. The current antiepileptic treatment methods cause severe side effects that endanger patient health and often preclude continued use. As a result, less toxic and more tolerable antiepileptic drugs (AEDs) are needed, especially since every individual responds differently to given treatment options.</p> <p>Methods</p> <p>Through a pharmacophore search, this study shows that a model previously designed to search for new classes of aromatase inhibitors is able to identify antiepileptic drugs from the set of drugs approved by the Food and Drug Administration. Chemical and structural similarity analyses were performed using five potent AIs, and these studies returned a set of AEDs that the model identifies as hits.</p> <p>Results</p> <p>The pharmacophore model returned 73% (19 out of 26) of the drugs used specifically to treat epilepsy and approximately 82% (51 out of 62) of the compounds with anticonvulsant properties. Therefore, this study supports the possibility of identifying AEDs with a pharmacophore model that had originally been designed to identify new classes of aromatase inhibitors. Potential candidates for anticonvulsant therapy identified in this manner are also reported. Additionally, the chemical and structural similarity between antiepileptic compounds and aromatase inhibitors is proved using similarity analyses.</p> <p>Conclusions</p> <p>This study demonstrates that a pharmacophore search using a model based on aromatase inhibition and the enzyme's structural features can be used to screen for new candidates for antiepileptic therapy. In fact, potent aromatase inhibitors and current antiepileptic compounds display significant - over 70% - chemical and structural similarity, and the similarity analyses performed propose a number of antiepileptic compounds with high potential for aromatase inhibition.</p

Mechanism of substrate specificity and insights into activation of phosphatidylinositol phosphate kinases

The phosphatidylinositol phosphate kinase family of enzymes is primarily responsible for converting singly phosphorylated phosphatidylinositol derivatives to phosphatidylinositol bisphosphates. As such, these kinases are central to many signaling and membrane trafficking processes in the eukaryotic cell. The three types of phosphatidylinositol phosphate kinases are homologous in sequence but differ in catalytic activities and biological functions. Type I and Type II kinases generate phosphatidylinositol 4,5-bisphosphate from phosphatidylinositol 4-phosphate and phosphatidylinositol 5-phosphate, respectively, whereas the Type III kinase produces phosphatidylinositol 3,5-bisphosphate from phosphatidylinositol 3-phosphate. Based on crystallographic analysis of the zebrafish Type I kinase PIP5Kα, we identified a subdomain unique to the kinase family that serves to recognize the monophosphate on the substrate. Our data indicate that the complex pattern of substrate recognition and phosphorylation results from the interplay between the monophosphate binding site and the specificity loop: the specificity loop functions to recognize different orientations of the inositol ring, whereas motifs flanking the phosphate binding Arg-244 determine whether phosphatidylinositol 3-phosphate is exclusively bound and phosphorylated at the 5-position. This work provides a thorough picture of how phosphatidylinositol phosphate kinases achieve their exquisite substrate specificity. In addition, the data presented here offers insights into activation of the Type I kinase, PIP5K, opening up a number of additional research questions yet to be addressed in this field

Targeting Glioma Stem Cells

Only a small fraction of the tumor cell population, glioma-initiating cells (GICs) help glioblastoma propagate, invade, evade immune recognition, repair DNA in response to radiation more efficiently, remodel the microenvironment for optimal growth, and actively pump out chemotherapies. Recent data hint that efforts toward GIC characterization and quantification can help predict patient outcomes, and yet the different subpopulations of GICs remain incompletely understood. A better understanding of GIC subtypes and functions proves critical for engineering targeted therapies. Challenges for doing so are discussed, and dopamine receptor antagonists are introduced as new means to enhance the efficacy of the current standard-of-care against GICs

Autoantibody mimicry of hormone action at the thyrotropin receptor.

Thyroid hormones are vital in metabolism, growth and development1. Thyroid hormone synthesis is controlled by thyrotropin (TSH), which acts at the thyrotropin receptor (TSHR)2. In patients with Graves' disease, autoantibodies that activate the TSHR pathologically increase thyroid hormone activity3. How autoantibodies mimic thyrotropin function remains unclear. Here we determined cryo-electron microscopy structures of active and inactive TSHR. In inactive TSHR, the extracellular domain lies close to the membrane bilayer. Thyrotropin selects an upright orientation of the extracellular domain owing to steric clashes between a conserved hormone glycan and the membrane bilayer. An activating autoantibody from a patient with Graves' disease selects a similar upright orientation of the extracellular domain. Reorientation of the extracellular domain transduces a conformational change in the seven-transmembrane-segment domain via a conserved hinge domain, a tethered peptide agonist and a phospholipid that binds within the seven-transmembrane-segment domain. Rotation of the TSHR extracellular domain relative to the membrane bilayer is sufficient for receptor activation, revealing a shared mechanism for other glycoprotein hormone receptors that may also extend to other G-protein-coupled receptors with large extracellular domains

Predicting HLA class II antigen presentation through integrated deep learning

Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4+ T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease