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Actinomycetes are a very important source of natural products for the pharmaceutical industry and other applications. Most of the strains belong to Streptomyces or related genera, partly because they are particularly amenable to growth in the laboratory and industrial fermenters. It is unlikely that chemical synthesis can fulfil the needs of the pharmaceutical industry for novel compounds so there is a continuing need to find novel natural products. An evolutionary perspective can help this process in several ways. Genome mining attempts to identify secondary metabolite biosynthetic clusters in DNA sequences, which are likely to produce interesting chemical entities. There are often technical problems in assembling the DNA sequences of large modular clusters in genome and metagenome projects, which can be overcome partially using information about the evolution of the domain sequences. Understanding the evolutionary mechanisms of modular clusters should allow simulation of evolutionary pathways in the laboratory to generate novel compounds.

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.

Modular biosynthetic clusters are responsible for the synthesis of many important pharmaceutical products. They include polyketide synthases (PKS clusters), non-ribosomal synthetases (NRPS clusters), and mixed clusters (containing both PKS and NRPS modules). The ClustScan database (CSDB) contains highly annotated descriptions of 170 clusters. The database has a hierarchical organization, which allows easy extraction of DNA and protein sequences of polypeptides, modules, and domains as well as an organization of the annotation so as to be able to predict the product chemistry to view it or export it in a standard SMILES format. The recombinant ClustScan database contains information about predicted recombinants between PKS clusters. The recombinants are generated by modeling homologous recombination and are associated with annotation and prediction of product chemistry automatically generated by the model. The database contains over 20,000 recombinants and is a resource for in silico approaches to detecting promising new compounds. Methods are available to construct the corresponding recombinants in the laboratory.

Soil bacteria live in a very competitive environment and produce many secondary metabolites; there appears to be strong selective pressure for evolution of new compounds. Secondary metabolites are the most important source of chemical structures for the pharmaceutical industry and an understanding of the evolutionary process should help in finding novel chemical entities. Modular polyketide synthases are a particularly interesting case for evolutionary studies, because much of the chemical structure can be predicted from DNA sequence. Previous evolutionary studies have concentrated on individual modules or domains and were not able to study the evolution of orthologues. This study overcame this problem by considering complete clusters as "organisms", so that orthologous modules and domains could be identified and used to characterise evolutionary pathways. Seventeen modular polyketide synthase clusters were identified that fell into six classes. Gene conversion within clusters was very common (affecting about 15 % of domains) and was detected by discordance in phylogenetic trees. An evolutionary model is proposed in which a single cross over between two different clusters (i.e. horizontal gene transfer) would generate a cluster of very different architecture with radically different chemical products; subsequent gene conversion and deletions would explore chemical variants. Two probable examples of such recombination were found. This model suggests strategies for detecting horizontal gene transfer in cluster evolution.

Modular polyketide synthases (PKSs) from Streptomyces and related genera of bacteria produce many important pharmaceuticals. A program called CompGen was developed to carry out in silico homologous recombination between gene clusters encoding PKSs and determine whether recombinants have cluster architectures compatible with the production of polyketides. The chemical structure of recombinant polyketides was also predicted. In silico recombination was carried out for 47 well-characterised clusters. The predicted recombinants would produce 11,796 different polyketide structures. The molecular weights and average degree of reduction of the chemical structures are dispersed around the parental structures indicating that they are likely to include pharmaceutically interesting compounds. The details of the recombinants and the chemical structures were entered in a database called r-CSDB. The virtual compound library is a useful resource for computer-aided drug design and chemoinformatics strategies for finding pharmaceutically relevant chemical entities. A strategy to construct recombinant Streptomyces strains to produce these polyketides is described and the critical steps of mobilizing large biosynthetic clusters and producing new linear cloning vectors are illustrated by experimental data.

An in silico model for homoeologous recombination between gene clusters encoding modular polyketide synthases (PKS) or non-ribosomal peptide synthetases (NRPS) was developed. This model was used to analyze recombination between 12 PKS clusters from Streptomyces species and related genera to predict if new clusters might give rise to new products. In many cases, there were only a limited number of recombination sites (about 13 per cluster pair), suggesting that recombination may pose constraints on the evolution of PKS clusters. Most recombination events occurred between pairs of ketosynthase (KS) domains, allowing the biosynthetic outcome of the recombinant modules to be predicted. About 30% of recombinants were predicted to produce polyketides. Four NRPS clusters from Streptomyces strains were also used for in silico recombination. They yielded a comparable number of recombinants to PKS clusters, but the adenylation (A) domains contained the largest proportion of recombination events; this might be a mechanism for producing new substrate specificities. The extreme G + C-content, the presence of linear chromosomes and plasmids, as well as the lack of a mutSL-mismatch repair system should favor production of recombinants in Streptomyces species.

The program package 'ClustScan' (Cluster Scanner) is designed for rapid, semi-automatic, annotation of DNA sequences encoding modular biosynthetic enzymes including polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS) and hybrid (PKS/NRPS) enzymes. The program displays the predicted chemical structures of products as well as allowing export of the structures in a standard format for analyses with other programs. Recent advances in understanding of enzyme function are incorporated to make knowledge-based predictions about the stereochemistry of products. The program structure allows easy incorporation of additional knowledge about domain specificities and function. The results of analyses are presented to the user in a graphical interface, which also allows easy editing of the predictions to incorporate user experience. The versatility of this program package has been demonstrated by annotating biochemical pathways in microbial, invertebrate animal and metagenomic datasets. The speed and convenience of the package allows the annotation of all PKS and NRPS clusters in a complete Actinobacteria genome in 2-3 man hours. The open architecture of ClustScan allows easy integration with other programs, facilitating further analyses of results, which is useful for a broad range of researchers in the chemical and biological sciences.