Abstract
Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pK i units).
Highlights
These observations overlap with previous findings: analysis of the Cytochrome P450 (CYP) Cytochrome P450 1A2 (1A2) crystal structure [20] indicated that Thr118 and Thr124 lie in a hydrophilic region that is considered important for the binding of polar substrates, while Phe226, Ala317, and Gly316 form a planar surface that is important for recognizing flat molecules
A dataset of IC50 values for 73 compounds characterized by a large chemical diversity was collected from three different literature sources, and the experimental uncertainty was estimated by measuring the inhibitory potency in-house for a sample of compounds from each source under the same conditions
35 compounds that covered a broad range of chemical diversity were used to calibrate our Linear Interaction Energy (LIE) model, which showed high correlation between calculated and observed values for the binding free energy (r2 = 0.68; q2CV = 0.66)
Summary
Mefenamic acid (!99%), tacrine (!99%), carvedilol (!98%), nifedipine (!98%), ellipticine, α-naphthoflavone (!97%), ticlopidine (!99%), 1-naphtol (!99%), 2-naphtol (!98%), 4-methoxy-benzaldehyde (!98%), 2-(p-tolyl)ethylamine (!97%) were purchased from SigmaAldrich (Schnelldorf, Germany); phenacetin was obtained from Brocades-ACF (Maarssen, the Netherlands). 7-Methoxyresorufin was synthesized by the method of Burke and Mayer [28] and final purity was assessed to be higher than 95%. The medoids of the clusters obtained (4 to 7 per ligand) were chosen as representative binding-conformations of the ligand in the CYP 1A2 active site These configurations were used to filter out potential non-competitive inhibitors and as starting poses for the MD simulations used in the LIE model, as described below. According to LIE theory [12], ΔGbind can be calculated from differences (ΔVEle and ΔVVdW) in the ensemble-averaged electrostatic hVElelig−surri and van der Waals interaction energies hVVdWlig−surri between the ligand and its surroundings when simulated in complex with the protein, or in the free state (water). For each simulation of the test set compounds, the Mahalanobis distance was computed from the center of the distribution of ΔVEle and ΔVVdW values obtained for the simulations used to train the LIE model. Ð6Þ where MAD is the median absolute deviation and z the 95 percentile of the cumulative normal distribution [56]
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