Prediction of twin-arginine signal peptides.

BACKGROUND: Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides. RESULTS: We have retrieved sequence data for Tat substrates in order to train a computational method for discrimination of Sec and Tat signal peptides. The TatP method is able to positively classify 91% of 35 known Tat signal peptides and 84% of the annotated cleavage sites of these Tat signal peptides were correctly predicted. This method generates far less false positive predictions on various datasets than using simple pattern matching. Moreover, on the same datasets TatP generates less false positive predictions than a complementary rule based prediction method. CONCLUSION: The method developed here is able to discriminate Tat signal peptides from cytoplasmic proteins carrying a similar motif, as well as from Sec signal peptides, with high accuracy. The method allows filtering of input sequences based on Perl syntax regular expressions, whereas hydrophobicity discrimination of Tat- and Sec-signal peptides is carried out by an artificial neural network. A potential cleavage site of the predicted Tat signal peptide is also reported. The TatP prediction server is available as a public web server at http://www.cbs.dtu.dk/services/TatP/.

NetAcet: prediction of N-terminal acetylation sites.

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.

Improved prediction of signal peptides: SignalP 3.0.

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

Feature-based prediction of non-classical and leaderless protein secretion.

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.

Development of in vitro transposon assisted signal sequence trapping and its use in screening Bacillus halodurans C125 and Sulfolobus solfataricus P2 gene libraries.

To identify genes encoding extracytosolic proteins, a minitransposon, TnSig, containing a signal-less beta-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 and EMBL ), 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.

Phylogenetic and functional analysis of the bacteriophage P1 single-stranded DNA-binding protein.

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 acquisition 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.