title: Computational differentiation of N-terminal signal peptides and transmembrane helices.
abstract: Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies.
authors.affiliation: ARC Centre in Bioinformatics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4066, Australia. z.yuan@imb.uq.edu.au
authors.forename: Zheng
authors.initials: Z
authors.lastname: Yuan
authors.forename: Melissa J
authors.initials: MJ
authors.lastname: Davis
authors.forename: Fasheng
authors.initials: F
authors.lastname: Zhang
authors.forename: Rohan D
authors.initials: RD
authors.lastname: Teasdale
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001426:Bacterial Proteins / Q000737:chemistry; D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000737:chemistry; D057927:Hydrophobic and Hydrophilic Interactions; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D021382:Protein Sorting Signals; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D001426:Bacterial Proteins; D008565:Membrane Proteins; D021382:Protein Sorting Signals