The Yin and Yang of redox regulation.

Mammalian cells produce reactive oxygen and nitrogen species (ROS/RNOS) in response to an oxidative environment. Powerful antioxidant mechanisms have been developed in order to avoid oxidative stress by contributing to the maintenance of redox homeostasis. Traditionally, accumulation of ROS/RNOS is considered deleterious for cells as it can lead to loss of cellular function, aging, and cell death. Consequently, ROS/RNOS imbalance has been implicated in the etiology and/or progression of numerous pathologies such as cardiovascular diseases, inflammation, and cancer. An interesting concept that has emerged more recently is that not only have cells developed efficient systems to cope with ROS/RNOS accumulation but they have also learned to profit of them under certain circumstances. This notion is supported by data showing that ROS/RNOS can act as signaling molecules affecting the function and activity of a multiplicity of protein kinases and phosphatases controlling cellular homeostasis. This review does not provide an exhaustive overview of molecular mechanisms linked to ROS/RNOS generation and processing but includes relevant examples highlighting the dichotomic nature of these small molecules and the multitude of effects elicited by their accumulation. This aspect of ROS/RNOS ought to be taken into account particularly in novel therapeutic setups that aim to achieve high efficiency and minimal or no side effects.

Virtual screening and prediction of site of metabolism for cytochrome P450 1A2 ligands.

With the availability of an increasing number of high resolution 3D structures of human cytochrome P450 enzymes, structure-based modeling tools are more readily used. In this study we explore the possibilities of using docking and scoring experiments on cytochrome P450 1A2. Three different questions have been addressed: 1. Binding orientations and conformations were successfully predicted for various substrates. 2. A virtual screen was performed with satisfying enrichment rates. 3. A classification of individual compounds into active and inactive was performed. It was found that while docking can be used successfully to address the first two questions, it seems to be more difficult to perform the classification. Different scoring functions were included, and the well-characterized water molecule in the active site was included in various ways. Results are compared to experimental data and earlier classification data using machine learning methods. The possibilities and limitations of using structure-based drug design tools for cytochrome P450 1A2 come to light and are discussed.