Publications

BACKGROUND:Ribonucleotide reductases (RNRs) catalyse the only known de novo pathway for deoxyribonucleotide synthesis, and are therefore essential to DNA-based life. While ribonucleotide reduction has a single evolutionary origin, significant differences between RNRs nevertheless exist, notably in cofactor requirements, subunit composition and allosteric regulation. These differences result in distinct operational constraints (anaerobicity, iron/oxygen dependence and cobalamin dependence), and form the basis for the classification of RNRs into three classes.DESCRIPTION:In RNRdb (Ribonucleotide Reductase database), we have collated and curated all known RNR protein sequences with the aim of providing a resource for exploration of RNR diversity and distribution. By comparing expert manual annotations with annotations stored in Genbank, we find that significant inaccuracies exist in larger databases. To our surprise, only 23% of protein sequences included in RNRdb are correctly annotated across the key attributes of class, role and function, with 17% being incorrectly annotated across all three categories. This illustrates the utility of specialist databases for applications where a high degree of annotation accuracy may be important. The database houses information on annotation, distribution and diversity of RNRs, and links to solved RNR structures, and can be searched through a BLAST interface. RNRdb is accessible through a public web interface at http://rnrdb.molbio.su.se.CONCLUSION:RNRdb is a specialist database that provides a reliable annotation and classification resource for RNR proteins, as well as a tool to explore distribution patterns of RNR classes. The recent expansion in available genome sequence data have provided us with a picture of RNR distribution that is more complex than believed only a few years ago; our database indicates that RNRs of all three classes are found across all three cellular domains. Moreover, we find a number of organisms that encode all three classes.

JTD Keywords: Enzymology (Biochemistry and Molecular Biophysics), Computer Applications (Computational Biology)

This 167-page book is volume 188 in the series 'Studies in computational intelligence.' This volume contain 9 extensive chapters written in English. This volume presents a collection of research advances in biologically inspired signal processing for chemical sensing. The olfactory system, and the gustatory system to a minor extent, has been taken in the last decades as a source of inspiration to develop artificial sensing systems. The recognition of odors by the olfactory system entails a number of signal processing functions such as preprocessing, dimensionality reduction, contrast enhancement, and classification. Using mathematical models to mimic the architecture of the olfactory system, these processing functions can be applied to chemical sensor signals. This book provides background on the olfactory system including a review on information processing in the insect olfactory system along with a proposed signal processing architecture based on the mammalian cortex. It also provides some bio-inspired approaches to process chemical sensor signals such as an olfactory mucosa to improve odor separation and a model of olfactory receptor neuron convergence to correlated sensor responses to an odor and his organoleptic properties. This book will useful to those working or studying in the areas of sensory reception and computational biology.

JTD Keywords: Nervous System (Neural Coordination), Computer Applications (Computational Biology), Sense Organs (Sensory Reception)