Topology assessment, G protein-coupled receptor (GPCR) prediction, and in vivo interaction assays to identify plant candidate GPCRs.

Genomic sequencing has provided a vast resource for identifying interesting genes, but often an exact "gene-of-interest" is unknown and is only described as putatively present in a genome by an observed phenotype, or by the known presence of a conserved signaling cascade, such as that facilitated by the heterotrimeric G-protein. The low sequence similarity of G protein-coupled receptors (GPCRs) and the absence of a known ligand with an associated high-throughput screening system in plants hampers their identification by simple BLAST queries or brute force experimental assays. Combinatorial bioinformatic analysis is useful in that it can reduce a large pool of possible candidates to a number manageable by medium or even low-throughput methods. Here we describe a method for the bioinformatic identification of candidate GPCRs from whole proteomes and their subsequent in vivo analysis for G-protein coupling using a membrane based yeast two-hybrid variant (Gookin et al., Genome Biol 9:R120, 2008). Rather than present the bioinformatic process in a format requiring scripts or computer programming knowledge, we describe procedures here in a simple, biologist-friendly outline that only utilizes the basic syntax of regular expressions.

Genome sequencing and analysis of the versatile cell factory Aspergillus niger CBS 513.88.

The filamentous fungus Aspergillus niger is widely exploited by the fermentation industry for the production of enzymes and organic acids, particularly citric acid. We sequenced the 33.9-megabase genome of A. niger CBS 513.88, the ancestor of currently used enzyme production strains. A high level of synteny was observed with other aspergilli sequenced. Strong function predictions were made for 6,506 of the 14,165 open reading frames identified. A detailed description of the components of the protein secretion pathway was made and striking differences in the hydrolytic enzyme spectra of aspergilli were observed. A reconstructed metabolic network comprising 1,069 unique reactions illustrates the versatile metabolism of A. niger. Noteworthy is the large number of major facilitator superfamily transporters and fungal zinc binuclear cluster transcription factors, and the presence of putative gene clusters for fumonisin and ochratoxin A synthesis.

Non-classical protein secretion in bacteria.

BACKGROUND: We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called "moon-lightning" proteins having more than one function. RESULTS: A thorough literature search was conducted to compile a list of currently known bacterial non-classically secreted proteins. Pattern finding methods were applied to the sequences in order to identify putative signal sequences or motifs responsible for their secretion. We have found no signal or motif characteristic to any majority of the proteins in the compiled list of non-classically secreted proteins, and conclude that these proteins, indeed, seem to be secreted in a novel fashion. However, we also show that the apparently non-classically secreted proteins are still distinguished from cellular proteins by properties such as amino acid composition, secondary structure and disordered regions. Specifically, prediction of disorder reveals that bacterial secretory proteins are more structurally disordered than their cytoplasmic counterparts. Finally, artificial neural networks were used to construct protein feature based methods for identification of non-classically secreted proteins in both Gram-positive and Gram-negative bacteria. CONCLUSION: We present a publicly available prediction method capable of discriminating between this group of proteins and other proteins, thus allowing for the identification of novel non-classically secreted proteins. We suggest candidates for non-classically secreted proteins in Escherichia coli and Bacillus subtilis. The prediction method is available online.