The secretome represents the ensemble of protein that a secreted in a given organism. Secretome analysis tools help predict the presence and location of signal peptides in protein sequences, which dictate if a protein is secreted or not.
Allows users to predict eukaryotic proteins location. TargetP is a web application that scores N-terminal pre-sequences in a submitted protein. The software indicates chloroplast transit peptide (cTP), mitochondrial targeting peptide (mTP) and secretory pathway signal peptide (SP) predicted localization. The application includes parameters which allow choosing between in Plants and Non-Plants version, personalized cutoffs and the possibility to determine cleavage sites.
Predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. SignalP is a neural network–based method which can discriminate signal peptides from transmembrane regions. The software incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
Combines transmembrane topology and signal peptide predictions. Phobius provides an easy and accurate mean to predict signal peptides and transmembrane topology from an amino acid sequence. Phobius makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions.
Provides prediction of protein subcellular localization for Gram-negative bacteria. PSORTb combines several methods, including homology analysis, identification of sorting signals and other motifs, and machine learning methods into an expert system for prediction of five subcellular localizations, given a complete amino acid sequence. This method is also able to handle situations where no prediction is possible, by assigning equal probabilities to each of the localization sites, again avoiding propagation of erroneous predictions.
An extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. WoLF PSORT not only provides subcellular localization prediction with competitive accuracy, but also provides detailed information relevant to protein localization to help users to form their own hypotheses.
Produces ab initio predictions of non-classical i.e. not signal peptide triggered protein secretion. The method queries a large number of other feature prediction servers to obtain information on various post-translational and localizational aspects of the protein, which are integrated into the final secretion prediction.