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This list of protein subcellular localisation prediction tools includes software, databases, and web services that are used for protein subcellular localization prediction. Some tools are included that are commonly used to infer location through predicted structural properties, such as signal peptide or transmembrane helices , and these tools ...
First of all, SOSUI looks for α helices that are relatively easy to predict, taking into account the known helical potentials of the given amino acid sequence(AAS). The much more difficult task is to differentiate between the α helices in soluble proteins and the ones in transmembrane proteins, the α helix being a very common secondary structure pattern in proteins.
A transmembrane domain (TMD) is a membrane-spanning protein domain.TMDs may consist of one or several alpha-helices or a transmembrane beta barrel.Because the interior of the lipid bilayer is hydrophobic, the amino acid residues in TMDs are often hydrophobic, although proteins such as membrane pumps and ion channels can contain polar residues.
Constituent amino-acids can be analyzed to predict secondary, tertiary and quaternary protein structure. This list of protein structure prediction software summarizes notable used software tools in protein structure prediction, including homology modeling, protein threading, ab initio methods, secondary structure prediction, and transmembrane helix and signal peptide prediction.
In addition to the protein domains, there are unusual transmembrane elements formed by peptides. A typical example is gramicidin A, a peptide that forms a dimeric transmembrane β-helix. [7] This peptide is secreted by gram-positive bacteria as an antibiotic. A transmembrane polyproline-II helix has not been reported in natural proteins ...
A step called domain parsing, or domain boundary prediction, is usually done first to split a protein into potential structural domains. As with the rest of tertiary structure prediction, this can be done comparatively from known structures [32] or ab initio with the sequence only (usually by machine learning, assisted by covariation). [33]
This causes the cross-prediction between them, which is a weakness of many transmembrane topology predictors. By predicting signal peptides and transmembrane helices simultaneously (Phobius [14]), the errors caused by cross-prediction are reduced and the performance is substantially increased. Another feature used to increase the accuracy of ...
MEMSAT [7] is an approach to predict the positions of transmembrane helix segments based on the recognition of the topological models of proteins. The method uses a set of statistical tables derived from well-characterized membrane protein data, and we have a dynamic programming algorithm to recognize the membrane topology models by maximizing ...