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Sesamin, a lignan component of sesame seed oil, has shown pharmacologic benefits, such as anti-oxidative and anti-inflammatory qualities. However, the amount of data available to the field is surprisingly sparse, as for instance there is no known spectroscopic characterization of sesamin. This work provides the first experimental infrared spectrum of sesamin. Sesamin powder was subjected to experimental Fourier-transform infrared spectroscopy, and the resulting spectrum was compared to quantum chemical calculations of sesamin's stereoisomers in various hydration states. Major peaks of sesamin were assigned vibrational modes through comparison of computed and observed spectra. Multiple sesamin species may be present in a typical powder sample, coexisting with potential trace hydration. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators. [ABSTRACT FROM AUTHOR]

Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, 'virtual docking calculations' are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel 'ensemble docking' calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be 'selected' by chemicals is known as 'conformational selection' process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process. [ABSTRACT FROM AUTHOR]