SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. This is a major advantage, because it makes SMARTCyp very fast. Other advantages are that experimental data are not a prerequisite to create the model, and it can easily be integrated with other methods to create models for other cytochrome P450 isoforms. Benchmarking tests on a database of 394 3A4 substrates show that SMARTCyp successfully identifies at least one metabolic site in the top two ranked positions 76% of the time. SMARTCyp is available for download at http://www.farma.ku.dk/p450.

We present a systematic investigation of how the axial ligand in heme proteins influences the geometry, electronic structure, and spin states of the active site, and the energies of the reaction cycles. Using the density functional B3LYP method and medium-sized basis sets, we have compared models with His, His+Asp, Cys, Tyr, and Tyr+Arg as found in myoglobin and hemoglobin, peroxidases, cytochrome P450, and heme catalases, respectively. We have studied 12 reactants and intermediates of the reaction cycles of these enzymes, including complexes with H(2)O, OH(-), O(2-), CH(3)OH, O(2), H(2)O(2), and HO(2)(-) in various formal oxidation states of the iron ion (II to V). The results show that His gives ~0.6 V higher reduction potentials than the other ligands. In particular, it is harder to reduce and protonate the O(2) complex with His than with the other ligands, in accordance with the O(2) carrier function of globins and the oxidative chemistry of the other proteins. For most properties, the trend Cys

We have estimated the activation energy for hydrogen abstraction by compound I in cytochrome P450 for a diverse set of 24 small organic substrates using state-of-the-art density functional theory (B3LYP). We then show that these results can be reproduced by computationally less demanding methods, for example, by using small organic mimics of compound I with both B3LYP and the semiempirical AM1 method (mean absolute error of 3-4 kJ/mol) or by calculating the bond dissociation energy, without relaxation of the radical (B3LYP) or estimated from three-point fit to a Morse potential (AM1; errors of 4 and 5 kJ/mol, respectively). We can assign activation energies of 74, 61, 53, 47, and 30 kJ/mol to primary carbons, secondary/tertiary carbons, carbons with adjacent sp(2) or aromatic groups, ethers/thioethers, and amines, respectively, which gives a very simple and predictive model. Finally, some of the less demanding methods are applied to study the CYP3A4 metabolism of progesterone and dextromethorphan.

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Supplementary data are available at Bioinformatics online.

RS-Predictor is a tool for creating pathway-independent, isozyme-specific site of metabolism (SOM) prediction models using any set of known cytochrome P450 substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1 and 3A4, which we believe is the largest publicly accessible collection of P450 ligands and metabolites ever released. A comprehensive investigation into the importance of different descriptor classes for predicting the regioselectivity of each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a DFT reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by StarDrop and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2(83.0%), 2A6(85.7%), 2B6(82.1%), 2C19(86.2%), 2C8(83.8%), 2C9(84.5%), 2D6(85.9%), 2E1(82.8%), 3A4(82.3%) and merged(86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as “metabolophores”: A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent,i isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

The oxidation and dealkylation of dimethylsulfoxide (DMSO), dimethylsulfide (DMS), and trimethylamine (TMA) by cytochrome P450 has been studied with density functional theory calculations. The results show that the oxidation reactions always occur on the doublet spin surface, whereas dealkylations can take place for both the doublet and quartet spin states. Moreover, DMS is more reactive than DMSO, and S-oxidation is more favorable than S-dealkylation, whereas N-dealkylation is more favorable than N-oxidation. This is in perfect agreement with experimental results, showing that density functional activation energies are reliable and comparable for widely different reactions with cytochrome P450.