Controlling the Circadian Clock with High Temporal Resolution through Photodosing

Circadian clocks, biological timekeepers that are present in almost every cell of our body, are complex systems whose disruption is connected to various diseases. Controlling cellular clock function with high temporal resolution in an inducible manner would yield an innovative approach for the circadian rhythm regulation. In the present study, we present structure-guided incorporation of photoremovable protecting groups into a circadian clock modifier, longdaysin, which inhibits casein kinase I (CKI). Using photodeprotection by UV or visible light (400 nm) as the external stimulus, we have achieved quantitative and light-inducible control over the CKI activity accompanied by an accurate regulation of circadian period in cultured human cells and mouse tissues, as well as in living zebrafish. This research paves the way for the application of photodosing in achieving precise temporal control over the biological timing and opens the door for chronophotopharmacology to deeper understand the circadian clock system

Methylation deficiency disrupts biological rhythms from bacteria to humans

メチル化と体内時計が生命誕生以来の密な関係にあることを発見 --生命の起源に学ぶヒト障害の新治療法--. 京都大学プレスリリース. 2020-05-27.The methyl cycle is a universal metabolic pathway providing methyl groups for the methylation of nuclei acids and proteins, regulating all aspects of cellular physiology. We have previously shown that methyl cycle inhibition in mammals strongly affects circadian rhythms. Since the methyl cycle and circadian clocks have evolved early during evolution and operate in organisms across the tree of life, we sought to determine whether the link between the two is also conserved. Here, we show that methyl cycle inhibition affects biological rhythms in species ranging from unicellular algae to humans, separated by more than 1 billion years of evolution. In contrast, the cyanobacterial clock is resistant to methyl cycle inhibition, although we demonstrate that methylations themselves regulate circadian rhythms in this organism. Mammalian cells with a rewired bacteria-like methyl cycle are protected, like cyanobacteria, from methyl cycle inhibition, providing interesting new possibilities for the treatment of methylation deficiencies

Publisher Correction: Methylation deficiency disrupts biological rhythms from bacteria to humans

From Springer Nature via Jisc Publications RouterHistory: registration 2020-05-27, pub-electronic 2020-06-04, online 2020-06-04, collection 2020-12Publication status: PublishedAn amendment to this paper has been published and can be accessed via a link at the top of the paper

Computergestützte Vorhersage von Sulfotransferase-Liganden zur Risikoabschätzung im Wirkstoffdesign

Sulfotransferases (SULTs) are among the predominant enzyme families of phase II metabolism. They transform endogenous molecules and environmental substances, such as drugs, into more hydrophilic entities serving detoxification. This transformation has also been associated with the formation of chemically reactive metabolites interacting with DNA. SULT subtype 1E1 (SULT1E1) shows high affinity towards estrogenic compounds and is involved in the regulation of endogenous estrogens such as estradiol. On the other hand, this enzyme can be strongly inhibited by environmental estrogens and endocrine disrupting compounds which deregulates metabolism reactions in the human body. The aim of the present study was to develop an in silico model for the prediction of SULT1E1 ligands, which allows identification of substrates and inhibitors to facilitate drug design and support risk assessment. All available crystal structures of SULT1E1 were analysed and compared to other major SULT subtypes to elucidate structural descriptors that influence ligand binding and substrate specificity. Findings from this structural investigation provided essential clues for subsequent prediction model development. In order to create a computer-based model for SULT1E1 ligand prediction, a specific workflow was designed using a combination of different in silico techniques. MD simulations were performed to investigate enzyme flexibility contributing to the broad substrate spectra of metabolic enzymes and to sample the conformational space. Diversity clustering of the trajectories produced an ensemble of protein conformations whose ligand binding sites differed from the original SULT1E1 crystal structure. In an ensemble docking approach, these protein conformations were combined with a ligand database of active SULT1E1 ligands, consisting of substrates, inhibitors, and concentration-dependent ligands (CDLs), to generate ligand- target complexes and to investigate their interaction patterns. The ensemble docking results were statistically and visually analysed based on 3D pharmacophore feature formation. Guided by statistical analysis of docking experiments, a selection of ligand-target complexes was chosen as a basis for 3D pharmacophore development. Eight specific 3D pharmacophores were developed that allow identification of diverse ligand classes (different activities and scaffolds) and types (substrates, inhibitors) of SULT1E1. The validated 3D pharmacophore ensemble showed a sensitivity of 60 % and a specificity of 98 %. For further refinement of the pharmacophore-based prediction of hit molecules, a substrate-filter and two classification models based on support vector machines (SVM) were created. The validated SVM models for inhibitor and substrate classification showed accuracies of 85 % and 91 %, respectively. In order to estimate the impact of SULT1E1 metabolism on current drugs, the final prediction model was applied to the DrugBank (a database comprising about 6,500 experimental and approved drugs) for virtual screening. From the 68 hit molecules, 28 % were identified as active SULT1E1 ligands through literature search. A selection of nine compounds was chosen for experimental validation including enzyme assays for inhibition and sulfonation. The experimental results confirmed the computer-based hypotheses and revealed previously unknown involvement of compounds listed in the DrugBank in biotransformation or inhibition of SULT1E1. The resulting prediction model of SULT1E1 could serve as an efficient in silico tool in early drug development for improved virtual screening of large databases and to provide structural alerts correlated with phase II metabolism during lead optimization. Furthermore, it potentially supports risk assessment of developed compounds in the pharmaceutical, nutritional, and cosmetic industry that bear the risk of being transformed into chemically reactive compounds damaging cellular DNA.Sulfotransferasen (SULTs) gehören zu den wichtigsten Enzymfamilien des Phase II Metabolismus. Mit ihrer Hilfe werden Xenobiotika in wasserlöslichere Zwischenprodukte umgewandelt, um schneller ausgeschieden werden zu können. SULT-katalysierte Reaktionen können jedoch auch zur Entstehung cancerogener Metaboliten führen. SULT Subtyp 1E1 (SULT1E1) weist Substratspezifität gegenüber Estrogenen auf und spielt daher eine wichtige Rolle in der Hormonregulation. Zudem kann das Enzym durch Estrogene und Endokrine Disruptoren stark inhibiert werden. Das Ziel dieser Studie war daher die Entwicklung eines computergestützten Modells zur Vorhersage von SULT1E1-Liganden welches die Identifizierung von Substraten und Inhibitoren erlaubt. Verfügbare Kristallstrukturen der SULT1E1 wurden analysiert und mit anderen SULT-Subtypen verglichen. Dies diente der Identifizierung von Merkmalen, welche die Substratspezifität beeinflussen und welche zur Entwicklung eines Vorhersagemodells eingesetzt werden können. Zur Erstellung des Modells wurde eine Sequenz von Methoden entwickelt und implementiert, die das breite Substratspektrum metabolischer Enzyme berücksichtigt. Im ersten Schritt wurden Moleküldynamiken des Enzyms in An- und Abwesenheit des Kofaktors simuliert. Auf Basis der Molekültrajektorien wurden Proteinkonformationen extrahiert, welche eine besonders diverse Ligandenbindestelle aufwiesen. Im nächsten Schritt wurden aktive Liganden der SULT1E1 (Inhibitoren, Substrate und Konzentrations-abhängige Liganden (CDLs)) in das Ensemble von Proteinen gedockt. Die daraus resultierenden Protein- Ligand Komplexe wurde statistisch und visuell unter Berücksichtigung von 3D Pharmakophordeskriptoren ausgewertet. Auf Grundlage dieser Analyse wurden acht spezifische 3D Pharmakophore erstellt, welche in der Lage sind SULT1E1-Liganden zu identifizieren. Die validierten 3D Pharmakophore weisen eine Sensitivität von 60 % und eine Spezifität von 98 % auf. Zur Optimierung der Pharmakophor-basierten Vorhersage wurden ein Substratfilter und zwei Klassifizierungsmodelle basierend auf Support Vector Machines (SVM) entwickelt. Die validierten SVM Modelle zur Inhibitor- und Substrat- Identifizierung weisen eine Genauigkeit von 85 % und 91 % auf. Das finale Vorhersagemodell für SULT1E1-Liganden wurde durch virtuelles Screening der DrugBank-Datenbank getestet, um das Ausmaß an SULT-Metabolismus an derzeitig erhältlichen oder in der Entwicklung stehenden Medikamenten zu untersuchen. Von etwa 6.500 gelisteten Molekülen in der Datenbank wurde 68 als aktive SULT1E1-Liganden identifiziert. Davon waren 28 % bereits in der Literatur bekannt. Neun der restlichen Substanzen wurden zur experimentellen Testung ausgewählt in der sowohl die Bestimmung von Inhibitoren als auch Substraten berücksichtigt wurde. Die experimentellen Ergebnisse standen im Einklang mit der computer-basierten Vorhersage und führten zur Identifizierung von Substanzen welche zuvor nicht mit SULT1E1-Aktivität in Verbindung gebracht wurden. Das hier entwickelte computerbasierte Vorhersagemodell des Enzyms SULT1E1 kann in frühen Phasen der Arzneistoffentwicklung eingesetzt werden, um potenziell metabolisch toxische Substanzen zu identifizieren. Desweiteren unterstützt das Modell die Risikobewertung bereits vermarkteter Substanzen der Pharma-, Ernährungs- und Kosmetikindustrie

Computational tools for in silico fragment-based drug design

Fragment-based strategy in drug design involves the initial discovery of low-molecular mass molecules. Owing to their small-size, fragments are molecular tools to probe specific sub-pockets within a protein active site. Once their interaction within the enzyme cavity is clearly understood and experimentally validated, they represent a unique opportunity to design potent and efficient larger compounds. Computer-aided methods can essentially support the identification of suitable fragments. In this review, available tools for computational drug design are discussed in the frame of fragmentbased approaches. We analyze and review (i) available commercial fragment libraries with respect to their properties and size, (ii) computational methods for the construction of such a library, (iii) the different strategies and software packages for the selection of the fragments with predicted affinity to a given target, and (iv) tools for the in silico linkage of fragments into an actual high-affinity lead structure candidate. © 2012 Bentham Science Publishers