Published online in Bioinformatics in February 2015
Jed Zaretzki, Michael Browning, Tyler B. Hughes, S. Joshua Swamidass
Motivation: Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA approved drugs. Medicinal chemists often want to know which atoms of a molecule— its metabolized sites—are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecule’s sites of metabolism. There is, however, much more data available at a lower resolution, where the exact site of metabolism is not known, but the region of the molecule that is oxidized is known. Until now, no site of metabolism models made use of region-resolution data.
Results: Here, we describe XenoSite-Region, the first reported method for training site-of-metabolism models with region-resolution data. Our approach uses the Expectation Maximization algorithm to train a site-of-metabolism model. Region-resolution metabolism data was simulated from a large site-of-metabolism dataset, containing 2,000 molecules with 3,400 metabolized and 30,000 un-metabolized sites and covering 9 Cytochrome P450 isozymes. When training on the same molecules (but with only region level information), we find that this approach yields models almost as accurate as models trained with atom-resolution data. Moreover, we find that atom-resolution trained models are more accurate when also trained with region-resolution data from additional molecules. Our approach, therefore, opens up a way to extend the applicable domain of site-of- metabolism models into larger regions of chemical space. This meets a critical need in drug development by tapping into underutilized data commonly available in most large drug companies.
The XenoSite webserver is available at https://swami.wustl.edu/xenosite.
The code and data is available at https://bitbucket.org/swamidass/xenosite-region/.