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.
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.
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.
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.
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 a predicting machine. NeuroPID is a robust method for identifying the neuropeptide precursors (NPPs) on a proteome-wide scale. This resource provides a list of candidates NPPs and neuromodulators at a genomic scale from unexplored proteomes. This method is valuable as a discovery platform for bioactive modulators. Its website provides a brief explanation on the different visualization options.
Predicts the site of cleavage between a signal sequence and a mature exported protein. sigcleave employs a scheme based on a weight-matrix approach that pinpoints signals in nucleic acid sequences. It can be used to discriminate between putative signal sequences and the N-terminal regions of cytosolic proteins. This tool can be extended to recognize correct cleavage to any unknown sequences.
Consists of a machine learning classifier for fungal effector prediction. EffectorP can predict significant enrichments of effectors in over 10 out of 13 sets of infection-induced proteins from diverse fungal pathogens. For each protein, the feature vectors are calculated using pepstats: frequencies of amino acids in the sequence; frequencies of amino acid classes in the sequence; molecular weight and length of the sequence; and protein net charge.
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.
Identifies signal peptides and their corresponding cleavage positions. PrediSi is a web server that allows users to analyze whole proteome datasets. The software’s algorithm is based on a position weight matrix approach. The web interface offers three matrices for the analysis of sequences from eukaryotes, Gram-positive and Gram-negative bacteria. Users can define the maximal length of the signal peptide and select the output format.
Predicts improved signal peptide. Signal-3L uses a hierarchical protocol that determines the existence of a signal peptide and then prioritizes potential cleavage sites. The tool uses statistical learning rules to select a final unique site in concertation with its evolution conservation score. The signal peptide obtained in useful to understand secretory proteins.
A method for predicting mammalian secreted proteins. SecretP is able to distinguish classically secreted proteins (CSPs), non-classically secreted proteins (NCSPs) and non-secreted proteins (NSPs) in mammals and bacteria.
Provides a platform to build reproducible and flexible pipelines for scalable secretome prediction. SecretSanta consists of a set of wrapper functions around a variety of command line tools. Users can pipe intermediate outputs between individual methods, run resource demanding stages in parallel, compare predictions between similar methods and trace back selected steps of the pipeline. It can facilitate comparisons of secretomes across multiple species or under various conditions and can lead to the discovery of novel classes of previously neglected secreted proteins.
An integrated database providing comprehensive information on type VI secretion systems (T6SSs) in bacteria. SecReT6 offers a unique, readily explorable archive of known and putative T6SSs, and cognate effectors found in bacteria. It currently contains data on 11 167 core T6SS components mapping to 906 T6SSs found in 498 bacterial strains representing 240 species, as well as a collection of over 600 directly relevant references. Also collated and archived were 1340 diverse candidate secreted effectors which were experimentally shown and/or predicted to be delivered by T6SSs into target eukaryotic and/or prokaryotic cells as well as 196 immunity proteins. A broad range of T6SS gene cluster detection and comparative analysis tools are readily accessible via SecReT6, which may aid identification of effectors and immunity proteins around the T6SS core components.
Simultaneously detects signal peptides and predicts the topology of transmembrane proteins. Philius is a dynamic Bayesian network (DBN)-based approach to transmembrane protein topology prediction. The software is inspired by Phobius and tackles the problem of discriminating among four basic types of proteins: globular (G), globular with a signal peptide (SP+G), transmembrane (TM), and transmembrane with a signal peptide (SP+TM). It also supplies a set of confidence scores with each prediction.