title: Combined prediction of Tat and Sec signal peptides with hidden Markov models.
abstract: BACKGROUND
  Computational prediction of signal peptides is of great importance in computational biology. In addition to the general secretory pathway (Sec), Bacteria, Archaea and chloroplasts possess another major pathway that utilizes the Twin-Arginine translocase (Tat), which recognizes longer and less hydrophobic signal peptides carrying a distinctive pattern of two consecutive Arginines (RR) in the n-region. A major functional differentiation between the Sec and Tat export pathways lies in the fact that the former translocates secreted proteins unfolded through a protein-conducting channel, whereas the latter translocates completely folded proteins using an unknown mechanism. The purpose of this work is to develop a novel method for predicting and discriminating Sec from Tat signal peptides at better accuracy.
  
  
  RESULTS
  We report the development of a novel method, PRED-TAT, which is capable of discriminating Sec from Tat signal peptides and predicting their cleavage sites. The method is based on Hidden Markov Models and possesses a modular architecture suitable for both Sec and Tat signal peptides. On an independent test set of experimentally verified Tat signal peptides, PRED-TAT clearly outperforms the previously proposed methods TatP and TATFIND, whereas, when evaluated as a Sec signal peptide predictor compares favorably to top-scoring predictors such as SignalP and Phobius. The method is freely available for academic users at http://www.compgen.org/tools/PRED-TAT/.
authors.affiliation: Department of Computer Science and Biomedical Informatics, University of Central Greece, Papasiopoulou 2-4, Lamia 35100, Greece. pbagos@ucg.gr
authors.forename: Pantelis G
authors.initials: PG
authors.lastname: Bagos
authors.forename: Elisanthi P
authors.initials: EP
authors.lastname: Nikolaou
authors.forename: Theodore D
authors.initials: TD
authors.lastname: Liakopoulos
authors.forename: Konstantinos D
authors.initials: KD
authors.lastname: Tsirigos
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008390:Markov Chains; D026901:Membrane Transport Proteins / Q000737:chemistry; D017510:Protein Folding; D021382:Protein Sorting Signals; D055571:Secretory Pathway
chemical_list: D026901:Membrane Transport Proteins; D021382:Protein Sorting Signals