Jurica Zucko

ClustScan: an integrated program package for the semi-automatic annotation of modular biosynthetic gene clusters and in silico prediction of novel chemical structures

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.

KEGG orthology-based annotation of the predicted proteome of Acropora digitifera: ZoophyteBase - an open access and searchable database of a coral genome

BackgroundContemporary coral reef research has firmly established that a genomic approach is urgently needed to better understand the effects of anthropogenic environmental stress and global climate change on coral holobiont interactions. Here we present KEGG orthology-based annotation of the complete genome sequence of the scleractinian coral Acropora digitifera and provide the first comprehensive view of the genome of a reef-building coral by applying advanced bioinformatics.DescriptionSequences from the KEGG database of protein function were used to construct hidden Markov models. These models were used to search the predicted proteome of A. digitifera to establish complete genomic annotation. The annotated dataset is published in ZoophyteBase, an open access format with different options for searching the data. A particularly useful feature is the ability to use a Google-like search engine that links query words to protein attributes. We present features of the annotation that underpin the molecular structure of key processes of coral physiology that include (1) regulatory proteins of symbiosis, (2) planula and early developmental proteins, (3) neural messengers, receptors and sensory proteins, (4) calcification and Ca2+-signalling proteins, (5) plant-derived proteins, (6) proteins of nitrogen metabolism, (7) DNA repair proteins, (8) stress response proteins, (9) antioxidant and redox-protective proteins, (10) proteins of cellular apoptosis, (11) microbial symbioses and pathogenicity proteins, (12) proteins of viral pathogenicity, (13) toxins and venom, (14) proteins of the chemical defensome and (15) coral epigenetics.ConclusionsWe advocate that providing annotation in an open-access searchable database available to the public domain will give an unprecedented foundation to interrogate the fundamental molecular structure and interactions of coral symbiosis and allow critical questions to be addressed at the genomic level based on combined aspects of evolutionary, developmental, metabolic, and environmental perspectives.

Draft Genome Sequence of Streptomyces rapamycinicus Strain NRRL 5491, the Producer of the Immunosuppressant Rapamycin

ABSTRACT Streptomyces rapamycinicus strain NRRL 5491 produces the important drug rapamycin. It has a large genome of 12.7 Mb, of which over 3 Mb consists of 48 secondary metabolite biosynthesis clusters.

Polyketide synthase genes and the natural products potential of Dictyostelium discoideum

MOTIVATIONnThe genome of the social amoeba Dictyostelium discoideum contains an unusually large number of polyketide synthase (PKS) genes. An analysis of the genes is a first step towards understanding the biological roles of their products and exploiting novel products.nnnRESULTSnA total of 45 Type I iterative PKS genes were found, 5 of which are probably pseudogenes. Catalytic domains that are homologous with known PKS sequences as well as possible novel domains were identified. The genes often occurred in clusters of 2-5 genes, where members of the cluster had very similar sequences. The D.discoideum PKS genes formed a clade distinct from fungal and bacterial genes. All nine genes examined by RT-PCR were expressed, although at different developmental stages. The promoters of PKS genes were much more divergent than the structural genes, although we have identified motifs that are unique to some PKS gene promoters.

New methods for medicinal chemistry - Universal gene cloning and expression systems for production of marine bioactive metabolites

Natural products from symbiotic or commensal associations between marine invertebrate and microbial organisms show exceptional promise as pharmaceuticals in many therapeutic areas. An economic and sustainable global market supply due to difficulty of synthesis is cited as the main obstacle for exploitation of these otherwise exciting marine bioactive compounds. Different strategies have been evoked to overcome this impediment as long-term harvesting of wild stocks from the environment is considered unsound, and other modes of production based on biosynthesis, such as aquaculture, have not yet been proven as reliable. One option is to clone the genes encoding the biosynthetic expression of a lead metabolite into a surrogate host suitable for industrial-scale fermentation. To facilitate this goal we are developing a universal system to clone and express genes responsible for biosynthesis of natural products from both eukaryotic and prokaryotic partners of marine symbioses. The ability to harness the complete meta-transcriptome of entire biosynthetic pathways is particularly valuable where the biogenesis of a target natural product occurring within a complex symbiotic association is unclear.

Predicting substrate specificity of adenylation domains of nonribosomal peptide synthetases and other protein properties by latent semantic indexing

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 8xa0Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.

Horizontal gene transfer and gene conversion drive evolution of modular polyketide synthases

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 15xa0% 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.

Bacterial diversity of polluted surface sediments in the northern Adriatic Sea

Samples were collected from sea sediments at seven sites in the northern Adriatic Sea that included six sites next to industrial complexes and one from a tourist site (recreational beach). The samples were assayed for alkanes and polycyclic aromatic hydrocarbons. The composition of the hydrocarbon samples suggested that industrial pollution was present in most cases. A sample from one site was also grown aerobically under crude oil enrichment in order to evaluate the response of indigenous bacterial populations to crude oil exposure. Analysis of 16S rRNA gene sequences showed varying microbial biodiversity depending on the level of pollution--ranging from low (200 detected genera) to high (1000+ genera) biodiversity, with lowest biodiversity observed in polluted samples. This indicated that there was considerable biodiversity in all sediment samples but it was severely restricted after exposure to crude oil selection pressure. Phylogenetic analysis of putative alkB genes showed high evolutionary diversity of the enzymes in the samples and suggested great potential for bioremediation and bioprospecting. The first systematic analysis of bacterial communities from sediments of the northern Adriatic Sea is presented, and it will provide a baseline assessment that may serve as a reference point for ecosystem changes and hydrocarbon degrading potential--a potential that could soon gain importance due to plans for oil exploitation in the area.

Evolutionary concepts in natural products discovery: what actinomycetes have taught us

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.

Databases of the thiotemplate modular systems (CSDB) and their in silico recombinants (r-CSDB)

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.