Jacob Engelbrecht

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequence. The method performs significantly better than previous prediction schemes and can easily be applied on genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server.

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/.

The specificity of the enzyme(s) catalysing the covalent link between the hydroxyl side chains of serine or threonine and the sugar moiety N-acetylgalactosamine (GalNAc) is unknown. Pattern recognition by artificial neural networks and weight matrix algorithms was performed to determine the exact position of in vivo O-linked GalNAc-glycosylated serine and threonine residues from the primary sequence exclusively. The acceptor sequence context for O-glycosylation of serine was found to differ from that of threonine and the two types were therefore treated separately. The context of the sites showed a high abundance of proline, serine and threonine extending far beyond the previously reported region covering positions -4 through +4 relative to the glycosylated residue. The O-glycosylation sites were found to cluster and to have a high abundance in the N-terminal part of the protein. The sites were also found to have an increased preference for three different classes of beta-turns. No simple consensus-like rule could be deduced for the complex glycosylation sequence acceptor patterns. The neural networks were trained on the hitherto largest data material consisting of 48 carefully examined mammalian glycoproteins comprising 264 O-glycosylation sites. For detection neural network algorithms were much more reliable than weight matrices. The networks correctly found 60-95% of the O-glycosylated serine/threonine residues and 88-97% of the non-glycosylated residues in two independent test sets of known glycoproteins. A computer server using E-mail for prediction of O-glycosylation sites has been implemented and made publicly available. The Internet address is NetOglyc@cbs.dtu.dk.

When preparing data sets of amino acid or nucleotide sequences it is necessary to exclude redundant or homologous sequences in order to avoid overestimating the predictive performance of an algorithm. For some time methods for doing this have been available in the area of protein structure prediction. We have developed a similar procedure based on pair-wise alignments for sequences with functional sites. We show how a correlation coefficient between sequence similarity and functional homology can be used to compare the efficiency of different similarity measures and choose a nonarbitrary threshold value for excluding redundant sequences. The impact of the choice of scoring matrix used in the alignments is examined. We demonstrate that the parameter determining the quality of the correlation is the relative entropy of the matrix, rather than the assumed (PAM or identity) substitution mode. Results are presented for the case of prediction of cleavage sites in signal peptides. By inspection of the false positives, several errors in the database were found. The procedure presented may be used as a general outline for finding a problem-specific similarity measure and threshold value for analysis of other functional amino acid or nucleotide sequence patterns.