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MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). In author's benchmark, MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that ...
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.
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.
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 ]
A table comparing four different scales for the hydrophobicity of an amino acid residue in a protein with the most hydrophobic amino acids on the top. A number of different hydrophobicity scales have been developed. [3] [1] [7] [8] [9] The Expasy Protscale website lists a total of 22 hydrophobicity scales. [10]
A transmembrane domain (TMD, TM domain) 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.
Computational methods exploit the sequence signatures of disorder to predict whether a protein is disordered, given its amino acid sequence. The table below, which was originally adapted from [1] and has been recently updated, shows the main features of software for disorder prediction. Note that different software use different definitions of ...
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 ...