Identification of endocrine disruptor biodegradation by integration of structure-activity relationship with pathway analysis

We present a SAR method that can predict estrogen-like endocrine disrupting chemical (EDC) activity as well as key biodegradation steps for detoxification. This method is based on a recent graph-mining algorithm developed by Kudo et al., which generates a set of descriptors from all potent chemical fragments (including rings). This method is novel in that it achieves chemical diversity in the training data set by sampling another data set of larger diversity. The model achieved an 83% accuracy prediction rate, and identified 1291 EDC candidates from the KEGG database. From this set of candidate compounds, bisphenol A was chosen for assay validation and biodegradation pathway analysis. Results showed that bisphenol A exhibited estrogen-like activity and was degraded in three distinct reactions. The prediction model provided information on the mechanism of the ligand-target binding, such as key functional groups involved. We focused on the enzyme commission number, which is useful for analyses of biodegradation pathways. Results identified oxygenases, ether hydrolases, and carbon-halide lyases as being important in the biodegradation pathway. This combined approach provided new information regarding the biodegradation of EDCs, and can potentially be extended to applications with transcriptomic, proteomic, and metabolomic data to provide a quick screen of biological activity and biodegradation pathway(s).