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MOTIVATION: Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing. RESULTS: SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program. AVAILABILITY AND IMPLEMENTATION: The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Aldehyde Oxidase (AO) is an enzyme involved in the metabolism of aldehydes and N-containing heterocyclic compounds. Many drug compounds contain heterocyclic moieties, and AO metabolism has lead to failure of several late-stage drug candidates. Therefore, it is important to take AO-mediated metabolism into account early in the drug discovery process, and thus, to have fast and reliable models to predict the site of metabolism (SOM). We have collected a dataset of 78 substrates of human AO with a total of 89 SOMs and 347 non-SOMs and determined atomic descriptors for each compound. The descriptors comprise NMR shielding and ESP charges from density functional theory (DFT), NMR chemical shift from ChemBioDraw, and Gasteiger charges from RDKit. Additionally, atomic accessibility was considered using 2D-SASA and relative span descriptors from SMARTCyp. Finally, stability of the product, the metabolite, was determined with DFT and also used as a descriptor. All descriptors have AUC larger than 0.75. In particular, descriptors related to the chemical shielding and chemical shift (AUC = 0.96) and ESP charges (AUC = 0.96) proved to be good descriptors. We recommend two simple methods to identify the SOM for a given molecule: 1) use ChemBioDraw to calculate the chemical shift or 2) calculate ESP charges or chemical shift using DFT. The first approach is fast but somewhat difficult to automate, while the second is more time-consuming, but can easily be automated. The two methods predict correctly 93% and 91%, respectively, of the 89 experimentally observed SOMs.

The vast diversity and membrane-bound nature of plant P450s makes it challenging to study the structural characteristics of this class of enzymes especially with respect to accurate intermolecular enzyme-substrate interactions. To address this problem we here apply a modified hybrid structure strategy for homology modeling of plant P450s. This allows for structural elucidation based on conserved motifs in the protein sequence and secondary structure predictions. We modeled the well-studied Sorghum bicolor cytochrome P450 CYP79A1 catalyzing the first step in the biosynthesis of the cyanogenic glucoside dhurrin. Docking experiments identified key regions of the active site involved in binding of the substrate and facilitating catalysis. Arginine 152 and threonine 534 were identified as key residues interacting with the substrate. The model was validated experimentally using site-directed mutagenesis. The new CYP79A1 model provides detailed insights into the mechanism of the initial steps in cyanogenic glycoside biosynthesis. The approach could guide functional characterization of other membrane-bound P450s and provide structural guidelines for elucidation of key structure-function relationships of other plant P450s.