Kinetic Mechanism of the Dechlorinating Flavin-dependent Monooxygenase HadA

The dramatic increase in heterogeneous types of biological data—in particular, the abundance of new protein sequences—requires fast and user-friendly methods for organizing this information in a way that enables functional inference. The most widely used strategy to link sequence or structure to function, homology-based function prediction, relies on the fundamental assumption that sequence or structural similarity implies functional similarity. New tools that extend this approach are still urgently needed to associate sequence data with biological information in ways that accommodate the real complexity of the problem, while being accessible to experimental as well as computational biologists. To address this, we have examined the application of sequence similarity networks for visualizing functional trends across protein superfamilies from the context of sequence similarity. Using three large groups of homologous proteins of varying types of structural and functional diversity—GPCRs and kinases from humans, and the crotonase superfamily of enzymes—we show that overlaying networks with orthogonal information is a powerful approach for observing functional themes and revealing outliers. In comparison to other primary methods, networks provide both a good representation of group-wise sequence similarity relationships and a strong visual and quantitative correlation with phylogenetic trees, while enabling analysis and visualization of much larger sets of sequences than trees or multiple sequence alignments can easily accommodate. We also define important limitations and caveats in the application of these networks. As a broadly accessible and effective tool for the exploration of protein superfamilies, sequence similarity networks show great potential for generating testable hypotheses about protein structure-function relationships.

Vehicle pollution is an increasing problem in the industrial world. Aromatic nitro compounds comprise a significant portion of the threat. In this review, the class includes nitro derivatives of benzene, biphenyls, naphthalenes, benzanthrone and polycyclic aromatic hydrocarbons, plus nitroheteroaromatic compounds. The numerous toxic manifestations are discussed. An appreciable number of drugs incorporate the nitroaromatic structure. The mechanistic aspects of both toxicity and therapy are addressed in the context of a unifying mechanism involving electron transfer, reactive oxygen species, oxidative stress and antioxidants.

Chlorophenols are chlorinated aromatic compound structures and are commonly found in pesticide preparations as well as industrial wastes. They are recalcitrant to biodegradation and consequently persistent in the environment. A variety of chlorophenols derivatives compounds are highly toxic, mutagenic and carcinogenic for living organisms. Biological transformation by microorganisms is one of the key remediation options that can be exploited to solve environmental pollution problems caused by these notorious compounds. The key enzymes in the microbial degradation of chlorophenols are the oxygenases and dioxygenases. These enzymes can be engineered for enhanced degradation of highly chlorinated aromatic compounds through directed evolution methods. This review underscores the mechanisms of chlorophenols biodegradation with the view to understanding how bioremediation processes can be optimized for cleaning up chloroaromatic contaminated environments. Copyright © 2011. Published by Elsevier Ltd.