2 resultados para Sars

em National Center for Biotechnology Information - NCBI


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The expression of virulence determinants in Staphylococcus aureus is controlled by global regulatory loci (e.g., sarA and agr). The sar (Staphylococcus accessory regulator) locus is composed of three overlapping transcripts (sarA P1, P3, and P2, transcripts initiated from the P1, P3, and P2 promoters, respectively), all encoding the 124-aa SarA protein. The level of SarA, the major regulatory protein, is partially controlled by the differential activation of the sarA promoters. We previously partially purified a 13.6-kDa protein, designated SarR, that binds to the sarA promoter region to down-modulate sarA transcription from the P1 promoter and subsequently SarA expression. SarR shares sequence similarity to SarA, and another SarA homolog, SarS. Here we report the 2.3 Å-resolution x-ray crystal structure of the dimeric SarR-MBP (maltose binding protein) fusion protein. The structure reveals that the SarR protein not only has a classic helix–turn–helix module for DNA binding at the major grooves, but also has an additional loop region involved in DNA recognition at the minor grooves. This interaction mode could represent a new functional class of the “winged helix” family. The dimeric SarR structure could accommodate an unusually long stretch of ≈27 nucleotides with two or four bending points along the course, which could lead to the bending of DNA by 90° or more, similar to that seen in the catabolite activator protein (CAP)–DNA complex. The structure also demonstrates the molecular basis for the stable dimerization of the SarR monomers and possible motifs for interaction with other proteins.

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We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.