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.

Autoantibodies to calpastatin (an endogenous inhibitor for calcium-dependent neutral protease, calpain) in systemic rheumatic diseases.

We identified an autoantibody that reacts with calpastatin [an inhibitor protein of the calcium-dependent neutral protease calpain (EC 3.4.22.17)]. In early immunoblot studies, sera from patients with rheumatoid arthritis (RA) recognized unidentified 60-, 45-, and 75-kDa proteins in HeLa cell extracts. To identify these autoantigens, we used patient sera to clone cDNAs from a lambda gt11 expression library. We isolated clones of four genes that expressed fusion proteins recognized by RA sera. The 1.2-kb cDNA insert (termed RA-6) appeared to encode a polypeptide corresponding to the 60-kDa antigen from HeLa cells, since antibodies bound to the RA-6 fusion protein also reacted with a 60-kDa HeLa protein. The deduced amino acid sequence of the RA-6 cDNA was completely identical with the C-terminal 178 amino acids of human calpastatin except for one amino acid substitution. Patient sera that reacted with the RA-6 also bound pig muscle calpastatin, and a monoclonal antibody to human calpastatin recognized the RA-6 fusion protein, confirming the identity of RA-6 with calpastatin. Moreover, the purified RA-6 fusion protein inhibited the proteolytic activity of calpain, and IgG from a serum containing anti-calpastatin antibodies blocked the calpastatin activity of the RA-6 fusion protein. Immunoblots of the RA-6 product detected autoantibodies to calpastatin in 57% of RA patients; this incidence was significantly higher than that observed in other systemic rheumatic diseases, including systemic lupus erythematosus (27%), polymyositis/dermatomyositis (24%), systemic sclerosis (38%), and overlap syndrome (29%). Thus, anti-calpastatin antibodies are present most frequently in patients with RA and may participate in pathogenic mechanisms of rheumatic diseases.