Karp, P.D., Krummenacker, M., Paley, S., and Wagg, J. Integrated Pathway/Genome Databases and Their Role in Drug Discovery. Trends in Biotechnology, vol. 17, no. 7, pp. 275-281, 1999.
Introduction
Integrated pathway/genome databases describe the genes and genome of an organism, as well as its predicted pathways, reactions, enzymes and metabolites. In conjunction with visualization and analysis software, these databases provide a framework for improved understanding of microbial physiology and for antimicrobial drug discovery. We describe pathway-based analyses of the genomes of a number of medically relevant microorganisms, and a novel software tool that provides visualization of gene expression data on a diagram showing the whole metabolic network of the microorganism. The method of inferring the function of a DNA or protein sequence by analogy to the functions of other similar sequences has had a profound impact on our ability to identify the functions of sequenced genes. Similar reasoning can be applied by analogy to biological pathways to identify the presence of known metabolic pathways in the annotated genome of an organism. Just as we can predict the function of an unknown sequence S by searching a reference database (DB) of sequences for those that are similar to S, we can also predict pathways from a sequenced genome by analogy to a reference DB of pathways.
A number of groups have developed techniques for predicting the metabolic pathways of an organism from its genome and for producing integrated pathway/genome databases that model the resulting predictions [1]. The techniques used range from manual analysis to automated computational analysis and the resulting databases vary according to both the types of information they contain and the software tools they make available for the querying, visualization and analysis of that information. Such projects include the KEGG project [2,3], ? the WIT project [4], ?? and a project at Pangea Systems that has produced the collection of microbial pathway/genome DBs described in this article. We will describe the information contained in these DBs, the methods used to create them, and some of the ways they may be exploited to support antimicrobial drug discovery.
