SVMtm: support vector machines to predict transmembrane segments.
A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of approximately 99%. This method can be used to complement current transmembrane helix prediction methods and can be used for consensus analysis of entire proteomes. The predictor is located at http://genet.imb.uq.edu.au/predictors/SVMtm.
ARC Centre in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, St. Lucia, 4072, Australia. z.yuan@imb.uq.edu.au
Zheng
Z
Yuan
John S
JS
Mattick
Rohan D
RD
Teasdale
D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D008565:Membrane Proteins / Q000737:chemistry*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D021382:Protein Sorting Signals; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D008565:Membrane Proteins; D021382:Protein Sorting Signals