The ChloroP server predicts the presence of chloroplast transit peptides (cTP) in protein sequences and the location of potential cTP cleavage sites.
DAS (Dense Alignment Surface) is based on low-stringency dot-plots of the query sequence against a set of library sequences - non-homologous membrane proteins - using a previously derived, special scoring matrix. The method provides a high precision hyrdophobicity profile for the query from which the location of the potential transmembrane segments can be obtained. The novelty of the DAS-TMfilter algorithm is a second prediction cycle to predict TM segments in the sequences of the TM-library.
Prediction of transmembranes helices and topology of proteins.
Helical TransMembrane Segment
Rotational Angle Prediction
MitoFates predicts mitochondrial presequence, a cleavable localization signal located in N-terminal, and its cleaved position.
MitoProt calculates the N-terminal protein region that can support a mitochondrial targeting sequence and the cleavage site.
NetNES 1.1 server predicts leucine-rich nuclear export signals (NES) in eukaryotic proteins using a combination of neural networks and hidden Markov models.
PATS identifies amino acid sequences that are potentially targeted to the apicoplast matrix of Plasmodium falciparum. Secondary analysis of candidate sequences is required for confirmation.
Prediction of transmembrane topology and signal peptides from the amino acid sequence of a protein.
PredictProtein integrates feature prediction for secondary structure, solvent accessibility, transmembrane helices, globular regions, coiled-coil regions, structural switch regions, B-values, disorder regions, intra-residue contacts, protein-protein and protein-DNA binding sites, sub-cellular localization, domain boundaries, beta-barrels, cysteine bonds, metal binding sites and disulphide bridges.
Predotar was designed for systematic screening of large batches of proteins for identifying putative targeting sequences, and recognizes the N-terminal targeting sequences of classically targeted precursor proteins. It provides a probability estimate as to whether the sequence contains a mitochondrial, plastid or ER targeting sequence.
PSORT family of programs for subcellular localization prediction
predict peroxisomal targeting signal 1 containing proteins
The SecretomeP 2.0 server 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.
SignalP 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. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
Classification and secondary structure srediction of membrane proteins.
TargetP 1.1 predicts the subcellular location of eukaryotic proteins. The location assignment is based on the predicted presence of any of the N-terminal presequences: chloroplast transit peptide (cTP), mitochondrial targeting peptide (mTP) or secretory pathway signal peptide (SP).
TatP 1.0 server predicts the presence and location of Twin-arginine signal peptide cleavage sites in bacteria. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of two artificial neural networks. A postfiltering of the output based on regular expressions is possible.
TMHMM is a membrane protein topology prediction method based on a hidden Markov model (HMM). It predicts transmembrane helices in proteins.
The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins