Density functional theory geometry optimizations and reduction potential calculations are reported for all five known oxidation states of [Fe(4)S(4)(SCH(3))(4)](n)()(-) (n = 0, 1, 2, 3, 4) clusters that form the active sites of iron-sulfur proteins. The geometry-optimized structures tend to be slightly expanded relative to experiment, with the best comparison found in the [Fe(4)S(4)(SCH(3))(4)](2)(-) model cluster, having bond lengths 0.03 A longer on average than experimentally observed. Environmental effects are modeled with a continuum dielectric, allowing the solvent contribution to the reduction potential to be calculated. The calculated protein plus solvent effects on the reduction potentials of seven proteins (including high potential iron proteins, ferredoxins, the iron protein of nitrogenase, and the "X", "A", and "B" centers of photosystem I) are also examined. A good correlation between predicted and measured absolute reduction potentials for each oxidation state of the cluster is found, both for relative potentials within a given oxidation state and for the absolute potentials for all known couples. These calculations suggest that the number of amide dipole and hydrogen bonding interactions with the Fe(4)S(4) clusters play a key role in modulating the accessible redox couple. For the [Fe(4)S(4)](0) (all-ferrous) system, the experimentally observed S = 4 state is calculated to lie lowest in energy, and the predicted geometry and electronic properties for this state correlate well with the EXAFS and Mössbauer data. Cluster geometries are also predicted for the [Fe(4)S(4)](4+) (all-ferric) system, and the calculated reduction potential for the [Fe(4)S(4)(SCH(3))(4)](1)(-)(/0) redox couple is in good agreement with that estimated for experimental model clusters containing alkylthiolate ligands.
Cytochromes P450 3A4, 2D6, and 2C9 metabolize a large fraction of drugs. Knowing where these enzymes will preferentially oxidize a molecule, the regioselectivity, allows medicinal chemists to plan how best to block its metabolism. We present QSAR-based regioselectivity models for these enzymes calibrated against compiled literature data of drugs and drug-like compounds. These models are purely empirical and use only the structures of the substrates, in contrast to those models that simulate a specific mechanism like hydrogen radical abstraction, and/or use explicit models of active sites. Our most predictive models use three substructure descriptors and two physical property descriptors. Descriptor importances from the random forest QSAR method show that other factors than the immediate chemical environment and the accessibility of the hydrogen affect regioselectivity in all three isoforms. The cross-validated predictions of the models are compared to predictions from our earlier mechanistic model (Singh et al. J. Med. Chem. 2003, 46, 1330-1336) and predictions from MetaSite (Cruciani et al. J. Med. Chem. 2005, 48, 6970-6979).
Aldehyde oxidase is a molybdenum hydroxylase that catalyzes the oxidation of aldehydes and nitrogen-containing heterocycles. The enzyme plays a dual role in the metabolism of physiologically important endogenous compounds and the biotransformation of xenobiotics. Using density functional theory methods, geometry optimization of tetrahedral intermediates of drugs and druglike compounds was examined to predict the likely metabolites of aldehyde oxidase. The calculations suggest that the lowest energy tetrahedral intermediate resulting from the initial substrate corresponds to the observed metabolite >or=90% of the time. Additional calculations were performed on a series of heterocyclic compounds where the products resulting from metabolism by xanthine oxidase and aldehyde oxidase differ in many instances. Again, the lowest energy tetrahedral intermediate corresponded to the observed product of aldehyde oxidase metabolism >or=90% for the compounds examined, while the observed products of xanthine oxidase were not well predicted.
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