dc.creator | Bagos, P. G. | en |
dc.creator | Nikolaou, E. P. | en |
dc.creator | Liakopoulos, T. D. | en |
dc.creator | Tsirigos, K. D. | en |
dc.date.accessioned | 2015-11-23T10:23:25Z | |
dc.date.available | 2015-11-23T10:23:25Z | |
dc.date.issued | 2010 | |
dc.identifier | 10.1093/bioinformatics/btq530 | |
dc.identifier.issn | 1367-4803 | |
dc.identifier.uri | http://hdl.handle.net/11615/26084 | |
dc.description.abstract | Motivation: 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/. | en |
dc.source | Bioinformatics | en |
dc.source.uri | <Go to ISI>://WOS:000283919800002 | |
dc.subject | ARGININE TRANSLOCATION PATHWAY | en |
dc.subject | GRAM-POSITIVE BACTERIA | en |
dc.subject | COMBINED | en |
dc.subject | TRANSMEMBRANE TOPOLOGY | en |
dc.subject | MEMBRANE-PROTEIN TOPOLOGY | en |
dc.subject | ESCHERICHIA-COLI | en |
dc.subject | CYTOPLASMIC MEMBRANE | en |
dc.subject | SORTING SIGNALS | en |
dc.subject | CLEAVAGE SITES | en |
dc.subject | SECRETION | en |
dc.subject | DATABASE | en |
dc.subject | Biochemical Research Methods | en |
dc.subject | Biotechnology & Applied Microbiology | en |
dc.subject | Computer Science, Interdisciplinary Applications | en |
dc.subject | Mathematical & | en |
dc.subject | Computational Biology | en |
dc.subject | Statistics & Probability | en |
dc.title | Combined prediction of Tat and Sec signal peptides with hidden Markov models | en |
dc.type | journalArticle | en |