1 - 50 of 54 results


Builds highly reliable core models (which usually correspond to a protein’s transmembrane (TM) region). Medeller is a method for coordinate generation specialized for membrane proteins (MPs). It thus builds a set of backbone coordinates for the majority of the structure. MEDELLER allows the user to provide more than one template protein. The high quality of MEDELLER’s core models is achieved by reliably selecting parts of the template structure that are similar to the correct target coordinates.


Provides an easy-to-use webserver for parameterising the angles of transmembrane helices based on Protein Data Bank (PDB) coordinates, with the helical orientations defined by the angles “tilt” and “swing”. AnglerFish is particularly useful for defining changes in structure between different states, including both symmetric and asymmetric transitions, and can be used to quantitate differences between related structures or different subunits within the same structure.


Calculates the physicochemical properties and amino acid composition. HELIQUEST uses the results to screen any databank in order to identify protein segments possessing similar features. It allows users to determine online the features of known helices and using the results as a starting point to extract equivalent helices in unexpected proteins. This tool can analyzed a sequence submitted by the user. The analyze is done by a sliding window. It uses an algorithm which detects the existence of an uninterrupted hydrophobic face of at least five residues adjacent on a helical wheel.


Predicts in-plane membrane (IPM) anchors. AmphipaSeek is available online on the NPS@ protein sequence analysis server. This tool uses a method based on a pattern recognition Support Vector Machine (SVM) with a dedicated kernel. It can also retrieve IPM anchors in sets of transmembrane proteins (e.g. PagP). It is a prediction method for IPM anchors in monotopic proteins using experimental data, and this method uses a set of 21 monotopic proteins reported as anchored in the membrane plane.

PredαTM and PredβTM / Transmembrane Region Predictors

Two independent algorithms, which predict the transmembrane regions of integral membrane proteins. PredαTM and PredβTM predict respectively the probable alpha helical transmembrane regions and the probable beta strand transmembrane regions present in a given protein sequence of an integral membrane protein. The underlying model is a SVM classifier trained on sequence data of transmembrane proteins with known structures. Given a protein sequence as input, the algorithms predict the probable transmembrane regions based on amino acid adjacency frequency and position specific preference of amino acids in the transmembrane regions.

TMM@ / Trans-Membrane alpha-helical Movement Analyzer

Studies the mobility of transmembrane α-helices. TMM@ uses normal mode analysis (NMA) to characterize the propensity of transmembrane α-helices to be displaced. Starting from a structure file at the protein data bank (PDB) format, the server computes the normal modes of the protein and identifies which helices in the bundle are the most mobile. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to further analyze the output data with their favourite software, raw results can also be downloaded.


A method for the prediction of protein membrane topology (intra- and extracellular sidedness) from multiply aligned amino acid sequences after determination of the membrane-spanning segments. The prediction technique relies on residue compositional differences in the protein segments exposed at each side of the membrane. Intra/extracellular ratios are calculated for the residue types Asn, Asp, Gly, Phe, Pro, Trp, Tyr, and Val, preferably found on the extracellular side, and for Ala, Arg, Cys, and Lys, mostly occurring on the intracellular side.


A method to predict rotational preferences of transmembrane helices to facilitate structural modeling. TMexpo first predicts lipid accessibility (the relative accessible surface area in lipid) by Support Vector Regression and predicts the classification of burial and exposed status of transmembrane helices (TMHs) by Support Vector Machine; and both models use evolutionary profiles, sequence conservation, helix insertion energy and biochemical properties as features. Then TMexpo calculates rotational angles of TMHs based on the predicted relative accessible surface area.

CSAH / Charged Single α-Helices

Offers two independent methods for Charged Single α-Helices (CSAH) detection in protein sequences. The CSAH server proposes SCAN4CSAH and FT_CHARGE methods. SCAN4CSAH is based on a scoring scheme favoring ionic interactions important in the stabilization of CSAHs, whereas FT_CHARGE identifies repeated charge patterns using Fourier transformation. Both methods report P-values denoting the probability of the observed score arising by chance. CSAH is available through a web server. Downloadable versions of both SCAN4CSAH and FT_CHARGE are provided along with a wrapper script to produce consensus and uniformly formatted output.


A transmembrane helices (TMH) predictor with excellent interpretability. The SOMRuler uses a self-organizing map (SOM) to learn helices distribution knowledge, which is encoded in the codebook vectors of the trained SOM, from the training samples. Human interpretable fuzzy rules are then extracted from the codebook vectors of the trained SOM. By extracting fuzzy rules from the learned knowledge rather than the original training samples, on the one hand, the computational burden of extracting fuzzy rules can be greatly reduced; on the other hand, the reliability of the extracted rules can also be enhanced since noise contained in the original samples can be smoothened by the learning procedure of SOM.

CCTOP / Consensus Constrained TOPology prediction

star_border star_border star_border star_border star_border
star star star star star
A web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. Given the amino acid sequence of a putative α-helical transmembrane protein, CCTOP predicts its topology i.e. localization of membrane spanning regions and orientation of segments between them. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format.


Assists in the determination of membrane protein topologies. TopPred is based on the construction of a hydrophobicity profile the can be used to recognize 'certain' and 'putative' transmembrane segments. It was tested on proteins which are synthesized in the cytoplasm and inserted in the prokaryotic inner plasma membrane, or the eukaryotic endoplasmic reticulum. This tool can serve for proteins synthesized in the mitochondrial matrix and inserted in the mitochondrial inner membrane.