Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Advances in Sustainable Catalysis: A Computational Perspective

The enormous challenge of moving our societies to a more sustainable future offers several exciting opportunities for computational chemists. The first principles approach to “catalysis by design” will enable new and much greener chemical routes to produce vital fuels and fine chemicals. This prospective outlines a wide variety of case studies to underscore how the use of theoretical techniques, from QM/MM to unrestricted DFT and periodic boundary conditions, can be applied to biocatalysis and to both homogeneous and heterogenous catalysts of all sizes and morphologies to provide invaluable insights into the reaction mechanisms they catalyze

Advances in sustainable catalysis: A computational perspective

The enormous challenge of moving our societies to a more sustainable future offers several exciting opportunities for computational chemists. The first principles approach to "catalysis by design" will enable new and much greener chemical routes to produce vital fuels and fine chemicals. This prospective outlines a wide variety of case studies to underscore how the use of theoretical techniques, from QM/MM to unrestricted DFT and periodic boundary conditions, can be applied to biocatalysis and to both homogeneous and heterogenous catalysts of all sizes and morphologies to provide invaluable insights into the reaction mechanisms they catalyze

Oxygen Activation and Radical Transformations in Heme Proteins and Metalloporphyrins

As a result of the adaptation of life to an aerobic environment, nature has evolved a panoply of metalloproteins for oxidative metabolism and protection against reactive oxygen species. Despite the diverse structures and functions of these proteins, they share common mechanistic grounds. An open-shell transition metal like iron or copper is employed to interact with O_2 and its derived intermediates such as hydrogen peroxide to afford a variety of metal–oxygen intermediates. These reactive intermediates, including metal-superoxo, -(hydro)peroxo, and high-valent metal–oxo species, are the basis for the various biological functions of O_2-utilizing metalloproteins. Collectively, these processes are called oxygen activation. Much of our understanding of the reactivity of these reactive intermediates has come from the study of heme-containing proteins and related metalloporphyrin compounds. These studies not only have deepened our understanding of various functions of heme proteins, such as O2 storage and transport, degradation of reactive oxygen species, redox signaling, and biological oxygenation, etc., but also have driven the development of bioinorganic chemistry and biomimetic catalysis. In this review, we survey the range of O_2 activation processes mediated by heme proteins and model compounds with a focus on recent progress in the characterization and reactivity of important iron–oxygen intermediates. Representative reactions initiated by these reactive intermediates as well as some context from prior decades will also be presented. We will discuss the fundamental mechanistic features of these transformations and delineate the underlying structural and electronic factors that contribute to the spectrum of reactivities that has been observed in nature as well as those that have been invented using these paradigms. Given the recent developments in biocatalysis for non-natural chemistries and the renaissance of radical chemistry in organic synthesis, we envision that new enzymatic and synthetic transformations will emerge based on the radical processes mediated by metalloproteins and their synthetic analogs

Pharmacogenetic modeling of human cytochrome P450 2D6; On the force of variation in inducing toxicity

Understanding the way in which drugs are metabolized by CYP2D6 and hence the underlying mechanisms that define potential toxicity is crucial to avoid adverse reactions. The high occurrence of CYP2D6 polymorphs enhances the complexity of the toxicity assessment of a drug candidate and should be tackled from early drug discovery phase on. The research described in this PhD thesis has been performed to provide novel fundamental insights regarding the metabolic activity of CYP2D6 wild-type and several polymorphs using various state-of-the-art in silico techniques. The results of the CYP2D6-focused studies enhance our knowledge regarding the enzyme particularities, and can be used to accelerate the development of CYP2D6 modeling tools with more accurate and reliable predictions