We present a Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides. PRED-LIPO is freely available at http://bioinformatics.biol.uoa.gr/PRED-LIPO/, and we anticipate that it will be a valuable tool for the experimentalists studying secreted proteins and lipoproteins from Gram-positive bacteria.
SciCrunch® is a data sharing and display platform. Anyone can create a custom portal where they can select searchable subsets of hundreds of data sources, brand their web pages and create their community. SciCrunch® will push data updates automatically to all portals on a weekly basis. User communities can also add their own data to SciCrunch®, however this is not currently a free service.