Glutamyl-tRNAGln amidotransferase in Deinococcus radiodurans may be confined to asparagine biosynthesis

Asparaginyl-tRNA (Asn-tRNA) and glutaminyl-tRNA (Gln-tRNA) are essential components of protein synthesis. They can be formed by direct acylation by asparaginyl-tRNA synthetase (AsnRS) or glutaminyl-tRNA synthetase (GlnRS). The alternative route involves transamidation of incorrectly charged tRNA. Examination of the preliminary genomic sequence of the radiation-resistant bacterium Deinococcus radiodurans suggests the presence of both direct and indirect routes of Asn-tRNA and Gln-tRNA formation. Biochemical experiments demonstrate the presence of AsnRS and GlnRS, as well as glutamyl-tRNA synthetase (GluRS), a discriminating and a nondiscriminating aspartyl-tRNA synthetase (AspRS). Moreover, both Gln-tRNA and Asn-tRNA transamidation activities are present. Surprisingly, they are catalyzed by a single enzyme encoded by three ORFs orthologous to Bacillus subtilis gatCAB. However, the transamidation route to Gln-tRNA formation is idled by the inability of the discriminating D. radiodurans GluRS to produce the required mischarged Glu-tRNAGln substrate. The presence of apparently redundant complete routes to Asn-tRNA formation, combined with the absence from the D. radiodurans genome of genes encoding tRNA-independent asparagine synthetase and the lack of this enzyme in D. radiodurans extracts, suggests that the gatCAB genes may be responsible for biosynthesis of asparagine in this asparagine prototroph.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

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.