Data Mining Assisted Discovery of Novel Organophosphorou

DESCRIPTION (provided by applicant): Organophosphorous (OP) neurotoxins such as paraoxon, sarin, soman, VX and Russian-VX, are a broad class of chemicals commonly used as chemical warfare (CW) agents. Currently, over 100,000 tons of chemical warfare agents are stockpiled worldwide. In addition, thousands-of-tons of OP compounds are used as pesticides and are annually released into the environment. Enzymes known as phosphotriesterases, commonly called organo-phosphate hydrolases (OPH's), are capable of hydrolyzing OP pesticides and CW agents into harmless products. Organophosphate hydrolases have recently become the focus of intense research efforts aimed at developing these enzymes into products for enzyme-based decontamination. However, currently available enzymes lack many of the desired properties such as solution solubility, thermal and pH stability and substrate specificity for all of the potential applications of OPH's. Numerous OPH homologues can be identified from the copious number of bacterial and archael genomes that have been either partially or completely sequenced. Unfortunately, many if not most are not active organophosphate hydrolases. We have developed a data mining method for identifying active OPH's. The method has already been used to discover two novel OPH's from the thermophiles Geobacillus stearothermophilus and Sulfolobus solfataricus. The current method of discriminating between active and inactive OPH's is dependent upon a limited pool size of active OPH's. We propose to isolate and analyze a series of five OPH homologues in order to refine our method of identification in order to maximize the pool size of active OPH's. In addition, the Geobacillus stearothermophilus and Sulfolobus solfataricus enzymes will be further characterized (including crystallization) for their suitability for use in enzyme-based decontamination systems.