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Contains fulltext : 51722.pdf (Publisher’s version ) (Closed access) 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.

We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough error-correction of a new data set, have improved the performance of the predictor significantly over SignalP version 2. In version 3, correctness of the cleavage site predictions has increased notably for all three organism groups, eukaryotes, Gram-negative and Gram-positive bacteria. The accuracy of cleavage site prediction has increased in the range 6-17% over the previous version, whereas the signal peptide discrimination improvement is mainly due to the elimination of false-positive predictions, as well as the introduction of a new discrimination score for the neural network. The new method has been benchmarked against other available methods. Predictions can be made at the publicly available web server

We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.

To identify genes encoding extracytosolic proteins, a minitransposon, TnSig, containing a signal-less β-lactamase (′ bla ) as reporter gene, was constructed and used for in vitro transposition of genomic libraries made in Escherichia coli . The ′ bla gene was cloned into a bacteriophage Mu minitransposon enabling translational fusions between ′ bla and target genes. Fusion of TnSig in the correct reading frame to a protein carrying transmembrane domains or signal peptides resulted in ampicillin resistance of the corresponding clone. Prokaryotic gene libraries from the alkaliphilic bacterium Bacillus halodurans C125 and the hyperthermophilic archaeon Sulfolobus solfataricus P2 were tagged with TnSig. The genomic sequences, which are publicly available (EMBL BA000004 and EMBL AE006641 ), were used for rapid open reading frame (ORF) identification and prediction of protein localisation in the cell. Genes for secreted proteins, transmembrane proteins and lipoproteins were successfully identified by this method. In contrast to previous transposon based identification strategies, the method described here is fast and versatile and essentially enables any selectable marker compatible library to be tagged. It is suited for identifying genes encoding extracytosolic proteins in gene libraries of a wide range of prokaryotic organisms.

Bacteriophage P1 encodes a single-stranded DNA-binding protein (SSB-P1), which shows 66% amino acid sequence identity to the SSB protein of the host bacterium Escherichia coli . A phylogenetic analysis indicated that the P1 ssb gene coexists with its E. coli counterpart as an independent unit and does not represent a recent acquirement of the phage. The P1 and E. coli SSB proteins are fully functionally interchangeable. SSB-P1 is nonessential for phage growth in an exponentially growing E. coli host, and it is sufficient to promote bacterial growth in the absence of the E. coli SSB protein. Expression studies showed that the P1 ssb gene is transcribed only, in an rpoS -independent fashion, during stationary-phase growth in E. coli . Mixed infection experiments demonstrated that a wild-type phage has a selective advantage over an ssb -null mutant when exposed to a bacterial host in the stationary phase. These results reconciled the observed evolutionary conservation with the seemingly redundant presence of ssb genes in many bacteriophages and conjugative plasmids.

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.

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.

Summary: We present here a neural network based method for prediction of N-terminal acetylation—by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs. Availability: The NetAcet prediction method is available as a public web server at http://www.cbs.dtu.dk/services/NetAcet/ Contact: nikob@cbs.dtu.dk Supplementary information: http://www.cbs.dtu.dk/services/NetAcet/