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Computational strategies to unravel secondary metabolic pathways in microorganisms

The ongoing genomic revolution, together with the application of computational tools, has provided the opportunity to mine whole genome and mixed-population metagenome sequences revealing numerous examples of 'cryptic' or 'orphan' biosynthetic gene clusters. The discovery of these secondary metabolic pathways, for which the encoded natural product is unknown, represents a treasure trove for gaining deeper insight into the evolution of chemical diversity and biological function. Several tools have been developed to assist these processes which, in the main, use local sequence alignment or motif searching to annotate based on finding the closest related sequences in a database. The reliability of such annotation depends, therefore, not only on the size and taxonomic diversity of the database against which a query sequence is compared, but also on the search criteria used to make the comparison. We have used a computational systems biology approach that integrates global sequence alignment and family profiling methods as a first step to overcome database composition bias. In addition, we have also developed an integrated set of computer programs to find modular gene clusters in substantial DNA data sets, which we call the ClustScan (Cluster Scanner) program package. The novelty of ClustScan over existing computer programs is the automatic knowledge-based prediction of substrate specificity and steriochemical outcomes of biosynthetic pathways, allowing linear and cyclic chemical structures to be deduced in silico. The versatility of our approaches to extract data on biosynthetic processes using genome sequences from marine invertebrate-microbial symbiotic assemblages and free-living terrestrial microorganisms will be discussed.

secondary metabolites; modular gene clusters; genomes; metagenomes; computer program package

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