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Abstract Background Automated function prediction has played a central role in determining the biological functions of bacterial proteins. Typically, protein function annotation relies on homology, and function is inferred from other proteins with similar sequences. This approach has become popular in bacterial genomics because it is one of the few methods that is practical for large datasets and because it does not require additional functional genomics experiments. However, the existing solutions produce erroneous predictions in many cases, especially when query sequences have low levels of identity with the annotated source protein. This problem has created a pressing need for improvements in homology-based annotation. Results We present an automated method for the functional annotation of bacterial protein sequences. Based on sequence similarity searches, BLANNOTATOR accurately annotates query sequences with one-line summary descriptions of protein function. It groups sequences identified by BLAST into subsets according to their annotation and bases its prediction on a set of sequences with consistent functional information. We show the results of BLANNOTATOR's performance in sets of bacterial proteins with known functions. We simulated the annotation process for 3090 SWISS-PROT proteins using a database in its state preceding the functional characterisation of the query protein. For this dataset, our method outperformed the five others that we tested, and the improved performance was maintained even in the absence of highly related sequence hits. We further demonstrate the value of our tool by analysing the putative proteome of Lactobacillus crispatus strain ST1. Conclusions BLANNOTATOR is an accurate method for bacterial protein function prediction. It is practical for genome-scale data and does not require pre-existing sequence clustering; thus, this method suits the needs of bacterial genome and metagenome researchers. The method and a web-server are available at http://ekhidna.biocenter.helsinki.fi/poxo/blannotator/.

Abstract Background Bacterial adhesive proteins, called adhesins, are frequently the decisive factor in initiation of a bacterial infection. Characterization of such molecules is crucial for the understanding of bacterial pathogenesis, design of vaccines and development of antibacterial drugs. Because adhesins are frequently difficult to express, their characterization has often been hampered. Alternative expression methods developed for the analysis of adhesins, e.g. surface display techniques, suffer from various drawbacks and reports on high-level extracellular secretion of heterologous proteins in Gram-negative bacteria are scarce. These expression techniques are currently a field of active research. The purpose of the current study was to construct a convenient, new technique for identification of unknown bacterial adhesive polypeptides directly from the growth medium of the Escherichia coli host and to identify novel proteinaceous adhesins of the model organism Staphylococcus aureus. Results Randomly fragmented chromosomal DNA of S. aureus was cloned into a unique restriction site of our expression vector, which facilitates secretion of foreign FLAG-tagged polypeptides into the growth medium of E. coli ΔfliCΔfliD, to generate a library of 1663 clones expressing FLAG-tagged polypeptides. Sequence and bioinformatics analyses showed that in our example, the library covered approximately 32% of the S. aureus proteome. Polypeptides from the growth medium of the library clones were screened for binding to a selection of S. aureus target molecules and adhesive fragments of known staphylococcal adhesins (e.g coagulase and fibronectin-binding protein A) as well as polypeptides of novel function (e.g. a universal stress protein and phosphoribosylamino-imidazole carboxylase ATPase subunit) were detected. The results were further validated using purified His-tagged recombinant proteins of the corresponding fragments in enzyme-linked immunoassay and surface plasmon resonance analysis. Conclusions A new technique for identification of unknown bacterial adhesive polypeptides was constructed. Application of the method on S. aureus allowed us to identify three known adhesins and in addition, five new polypeptides binding to human plasma and extracellular matrix proteins. The method, here used on S. aureus, is convenient due to the use of soluble proteins from the growth medium and can in principle be applied to any bacterial species of interest.

Abstract Background Considerable interest in the bioconversion of lignocellulosic biomass into ethanol has led to metabolic engineering of Saccharomyces cerevisiae for fermentation of xylose. In the present study, the transcriptome and proteome of recombinant, xylose-utilising S. cerevisiae grown in aerobic batch cultures on xylose were compared with those of glucose-grown cells both in glucose repressed and derepressed states. The aim was to study at the genome-wide level how signalling and carbon catabolite repression differ in cells grown on either glucose or xylose. The more detailed knowledge whether xylose is sensed as a fermentable carbon source, capable of catabolite repression like glucose, or is rather recognised as a non-fermentable carbon source is important for further engineering this yeast for more efficient anaerobic fermentation of xylose. Results Genes encoding respiratory proteins, proteins of the tricarboxylic acid and glyoxylate cycles, and gluconeogenesis were only partially repressed by xylose, similar to the genes encoding their transcriptional regulators HAP4, CAT8 and SIP1-2 and 4. Several genes that are repressed via the Snf1p/Mig1p-pathway during growth on glucose had higher expression in the cells grown on xylose than in the glucose repressed cells but lower than in the glucose derepressed cells. The observed expression profiles of the transcription repressor RGT1 and its target genes HXT2-3, encoding hexose transporters suggested that extracellular xylose was sensed by the glucose sensors Rgt2p and Snf3p. Proteome analyses revealed distinct patterns in phosphorylation of hexokinase 2, glucokinase and enolase isoenzymes in the xylose- and glucose-grown cells. Conclusion The results indicate that the metabolism of yeast growing on xylose corresponds neither to that of fully glucose repressed cells nor that of derepressed cells. This may be one of the major reasons for the suboptimal fermentation of xylose by recombinant S. cerevisiae strains. Phosphorylation of different isoforms of glycolytic enzymes suggests that regulation of glycolysis also occurred at a post-translational level, supporting prior findings.

Abstract Background Sequence motifs representing transcription factor binding sites (TFBS) are commonly encoded as position frequency matrices (PFM) or degenerate consensus sequences (CS). These formats are used to represent the characterised TFBS profiles stored in transcription factor databases, as well as to represent the potential motifs predicted using computational methods. To fill the gap between the known and predicted motifs, methods are needed for the post-processing of prediction results, i.e. for matching, comparison and clustering of pre-selected motifs. The computational identification of over-represented motifs in sets of DNA sequences is, in particular, a task where post-processing can dramatically simplify the analysis. Efficient post-processing, for example, reduces the redundancy of the motifs predicted and enables them to be annotated. Results In order to facilitate the post-processing of motifs, in both PFM and CS formats, we have developed a tool called Matlign. The tool aligns and evaluates the similarity of motifs using a combination of scoring functions, and visualises the results using hierarchical clustering. By limiting the number of distinct gaps created (though, not their length), the alignment algorithm also correctly aligns motifs with an internal spacer. The method selects the best non-redundant motif set, with repetitive motifs merged together, by cutting the hierarchical tree using silhouette values. Our analyses show that Matlign can reliably discover the most similar analogue from a collection of characterised regulatory elements such that the method is also useful for the annotation of motif predictions by PFM library searches. Conclusion Matlign is a user-friendly tool for post-processing large collections of DNA sequence motifs. Starting from a large number of potential regulatory motifs, Matlign provides a researcher with a non-redundant set of motifs, which can then be further associated to known regulatory elements. A web-server is available at http://ekhidna.biocenter.helsinki.fi/poxo/matlign.