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.

The sulfur controller-2 negative regulatory gene of Neurospora crassa encodes a protein with beta-transducin repeats.

The sulfur regulatory system of Neurospora crassa is composed of a set of structural genes involved in sulfur catabolism controlled by a genetically defined set of trans-acting regulatory genes. These sulfur regulatory genes include cys-3+, which encodes a basic region-leucine zipper transcriptional activator, and the negative regulatory gene scon-2+. We report here that the scon-2+ gene encodes a polypeptide of 650 amino acids belonging to the expanding beta-transducin family of eukaryotic regulatory proteins. Specifically, SCON2 protein contains six repeated G beta-homologous domains spanning the C-terminal half of the protein. SCON2 represents the initial filamentous fungal protein identified in the beta-transducin group. Additionally, SCON2 exhibits a specific amino-terminal domain that potentially defines another subfamily of beta-transducin homologs. Expression of the scon-2+ gene has been examined using RNA hybridization and gel mobility-shift analysis. The dependence of scon-2+ expression on CYS3 function and the binding of CYS3 to the scon-2+ promoter indicate the presence of an important control loop within the N. crassa sulfur regulatory circuit involving CYS3 activation of scon-2+ expression. On the basis of the presence of beta-transducin repeats, the crucial role of SCON2 in the signal-response pathway triggered by sulfur limitation may be mediated by protein-protein interactions.