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Members of the Swi2/Snf2 (switch/sucrose non-fermentable) family depend on their ATPase activity to mobilize nucleic acid-protein complexes for gene expression. In bacteria, RapA is an RNA polymerase (RNAP)-associated Swi2/Snf2 protein that mediates RNAP recycling during transcription. It is known that the ATPase activity of RapA is stimulated by its interaction with RNAP. It is not known, however, how the RapA-RNAP interaction activates the enzyme. Previously, we determined the crystal structure of RapA. The structure revealed the dynamic nature of its N-terminal domain (Ntd), which prompted us to elucidate the solution structure and activity of both the full-length protein and its Ntd-truncated mutant (RapAΔN). Here, we report the ATPase activity of RapA and RapAΔN in the absence or presence of RNAP and the solution structures of RapA and RapAΔN either ligand-free or in complex with RNAP. Determined by small-angle x-ray scattering, the solution structures reveal a new conformation of RapA, define the binding mode and binding site of RapA on RNAP, and show that the binding sites of RapA and σ70 on the surface of RNAP largely overlap. We conclude that the ATPase activity of RapA is inhibited by its Ntd but stimulated by RNAP in an allosteric fashion and that the conformational changes of RapA and its interaction with RNAP are essential for RNAP recycling. These and previous findings outline the functional cycle of RapA, which increases our understanding of the mechanism and regulation of Swi2/Snf2 proteins in general and of RapA in particular. The new structural information also leads to a hypothetical model of RapA in complex with RNAP immobilized during transcription. PMID:26272746
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Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C ++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multi-layer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and post-processing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.