Structural bioinformatics Extending P450 site-of-metabolism models with region-resolution data

Abstract

Motivation: Cytochrome P450s are a family of enzymes responsible for the metabolism of approxi-mately 90 % of FDA-approved drugs. Medicinal chemists often want to know which atoms of a mol-ecule—its metabolized sites—are oxidized by Cytochrome P450s in order to modify their metabol-ism. 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 re-gion-resolution data. Results: Here, we describe XenoSite-Region, the first reported method for training site-of-metabol-ism models with region-resolution data. Our approach uses the Expectation Maximization algo-rithm to train a site-of-metabolism model. Region-resolution metabolism data was simulated from a large site-of-metabolism dataset, containing 2000 molecules with 3400 metabolized and 30 000 un-metabolized sites and covering nine 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-me-tabolism models into larger regions of chemical space. This meets a critical need in drug develop-ment by tapping into underutilized data commonly available in most large drug companies. Availability and implementation: The algorithm, data and a web server are available a