In silico identification of potential inhibitors for human aurora kinase b

Cell cycle progression through mitosis and meiosis involves regulation by serine/threonine kinases from the aurora family. Aurora kinase b (Aurkb) is mainly involved in the proper segregation of chromosomes during mitosis as well as meiosis. However, over expression of Aurkb leads to the unequal distribution of genetic information creating aneuploid cells, a hallmark of cancer. Thus, Aurkb can be used as an effective molecular target for computer-aided drug discovery against cancer. Existing Aurkb inhibitors are less efficient, hence an in silico work was carried out to identify novel potent inhibitors. Three published inhibitors azd1152, zm447439 and N-(4-{[6-methoxy-7-(3-morpholin-4-ylpropoxy) quinazolin- 4-yl] amino} phenyl) benzamide were subjected to high throughput virtual screening of over 1 million entries from a ligand info meta database, to generate a 1161 compound library. The crystal structure was optimized and energy was minimized applying an OPLS force field in Maestro v9.0. Molecular docking using Glide was performed to predict the binding orientation of the prepared ligand molecule into a grid of 20*20*20 Å created around the centroid of the optimized human Aurkb protein. Nine lead molecules with good binding affinity with human Aurkb were identified. In silico pharmacokinetics study for these nine lead molecules has shown no ADME violation. Analysis of lead ‘1’- human Aurkb docking complex has revealed a XP Gscore of -10.20 kcal/mol with a highly stabilized hydrogen bond network with Asp218 and Ala157 and good Van der wall interactions. The docking complex coincides well with the native co- crystallized human Aurkb and inhibitor zm447439 complex. Thus, lead 1 would be highly useful for developing potential drug molecules for the treatment of cancer

Spectrophotometry of comet Kohoutek (1973f) during pre-perihelion period

Scans of the head of comet Kohoutek obtained during the pre-perihelion period with a photoelectric spectrum scanner are discussed. The mean relative flux distributions and the continuum energy distribution of the head of comet Kohoutek are given and normalized to 479 nm. The emission features of CN, C3, Ch, and the principal Swan band sequences of C2 and Na are identified and discussed. The adopted monochromatic values relative to 479 nm are given and plotted along with the energy distribution of the sun

Using R-based VOStat as a low resolution spectrum analysis tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey

Some statistical and computational challenges, and opportunities in astronomy

The data complexity and volume of astronomical findings have increased in recent decades due to major technological improvements in instrumentation and data collection methods. The contemporary astronomer is flooded with terabytes of raw data that produce enormous multidimensional catalogs of objects (stars, galaxies, quasars, etc.) numbering in the billions, with hundreds of measured numbers for each object. The astronomical community thus faces a key task: to enable efficient and objective scientific exploitation of enormous multifaceted data sets and the complex links between data and astrophysical theory. In recognition of this task, the National Virtual Observatory (NVO) initiative recently emerged to federate numerous large digital sky archives, and to develop tools to explore and understand these vast volumes of data. The effective use of such integrated massive data sets presents a variety of new challenging statistical and algorithmic problems that require methodological advances. An interdisciplinary team of statisticians, astronomers and computer scientists from The Pennsylvania State University, California Institute of Technology and Carnegie Mellon University is developing statistical methodology for the NVO. A brief glimpse into the Virtual Observatory and the work of the Penn State-led team is provided here