Bioprospecting for Genes Encoding Hydrocarbon-Degrading Enzymes from Metagenomic Samples Isolated from Northern Adriatic Sea Sediments.

Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.

MEGGASENSE - The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for the Construction of Sequence Data Warehouses.

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

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

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.

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

BACKGROUND: Contemporary 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. DESCRIPTION: Sequences 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. CONCLUSIONS: We 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.

The effect of starvation stress on Lactobacillus brevis L62 protein profile determined by de novo sequencing in positive and negative mass spectrometry ion mode.

RATIONALE: We describe a novel negative chemically activated fragmentation/positive chemically activated fragmentation (CAF-/CAF+) technique for protein identification. The technique was used to investigate Lactobacillus brevis adaptation to nutrient deprivation. METHODS: The CAF-/CAF+ method enables de novo sequencing of derivate peptides with negative and positive ion mode matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS). Peptide sequences obtained from MS/MS spectra were matched against the National Center for Biotechnology Information (NCBI) non-redundant (nr) database and confirmed by the mass spectrometry data of elucidated peptide mass sequences derived from the annotated genome. This improved protein identification method highlighted 36 differentially expressed proteins in the proteome of L. brevis after 75 days of starvation. RESULTS: The results revealed the key differences in the metabolic pathways that are responsible for the survival of L. brevis in a hostile environment. Proteomics analysis demonstrated that numerous proteins engaged in glucose and amino-acid catabolizing pathways, glycerolipid metabolizing pathways, and stress-response mechanisms are differentially expressed after long-term starvation. Amino acid and proteomics analysis indicated that starved L. brevis metabolized arginine, glycine, and histidine from dead cells as alternative nutrient sources. The production of lactic acid also varied between the parent cells and the starved cells. CONCLUSIONS: Differentially expressed proteins identified exclusively by peptide sequence reading provided promising results for CAF-/CAF+ implementation in a standard proteomics workflow (e.g., biomarker and mutation discovery and biotyping). The practical performance of a reliable de novo sequencing technique in routine proteomics analysis is emphasized in this article.

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.

Recombinatorial biosynthesis of polyketides.

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.

A novel docking domain interface model predicting recombination between homoeologous modular biosynthetic gene clusters.

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.

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.