Department of Computer Science and Biomedical Informatics, University of Central Greece, Papasiopoulou 2-4, Lamia 35100, Greece.
Bioinformatics. 2010 Nov 15;26(22):2811-7. doi: 10.1093/bioinformatics/btq530. Epub 2010 Sep 16.
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.
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/.
计算预测信号肽在计算生物学中具有重要意义。除了一般的分泌途径（Sec）外，细菌、古菌和叶绿体还拥有另一种主要途径，即利用双精氨酸转运体（Tat），它识别带有两个连续精氨酸（RR）特征模式的较长且疏水性较弱的 n 区信号肽。Sec 和 Tat 输出途径之间的一个主要功能区别在于，前者通过蛋白质导通道转运未折叠的分泌蛋白，而后者则利用未知的机制转运完全折叠的蛋白质。这项工作的目的是开发一种新的方法，以更高的准确性预测和区分 Sec 和 Tat 信号肽。
我们报告了一种新方法 PRED-TAT 的开发，该方法能够区分 Sec 和 Tat 信号肽并预测它们的切割位点。该方法基于隐马尔可夫模型，具有适用于 Sec 和 Tat 信号肽的模块化架构。在独立的实验验证的 Tat 信号肽测试集上，PRED-TAT 明显优于先前提出的 TatP 和 TATFIND 方法，而作为 Sec 信号肽预测器进行评估时，与 SignalP 和 Phobius 等顶级预测器相比具有优势。该方法可供学术用户免费使用，网址为 http://www.compgen.org/tools/PRED-TAT/。
