Combined prediction of Tat and Sec signal peptides with hidden Markov models
Combined prediction of Tat and Sec signal peptides with hidden Markov models
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DOI：
10.1093/bioinformatics/btq530
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发表时间：
2010-11-01
期刊：
影响因子：
5.8
通讯作者：
Tsirigos, Konstantinos D.
中科院分区：
文献类型：
--
作者：
Bagos, Pantelis G.;Nikolaou, Elisanthi P.;Tsirigos, Konstantinos D.
动机：信号肽的计算预测在计算生物学中非常重要。除了一般的分泌途径（Sec）之外，细菌、细菌和叶绿体具有利用双精氨酸移位酶（达特）的另一个主要途径，该双精氨酸移位酶识别在n-区中携带两个连续精氨酸（RR）的独特模式的较长且较不疏水的信号肽。Sec和达特输出途径之间的主要功能差异在于，前者通过蛋白传导通道转运未折叠的分泌蛋白，而后者使用未知的机制转运完全折叠的蛋白。本研究的目的是建立一种新的预测和区分Sec和达特信号肽的方法。结果：我们建立了一种新的预测和区分Sec和达特信号肽的方法PRED-TAT。该方法是基于隐马尔可夫模型，并拥有一个模块化的架构，适用于Sec和达特信号肽。在实验验证的达特信号肽的独立测试集上，PRED-TAT明显优于先前提出的方法TatP和TATFIND，而当作为Sec信号肽预测因子评估时，与得分最高的预测因子如SignalP和Phobius相比是有利的。学术用户可以在http://www.compgen.org/tools/PRED-TAT/上免费获得该方法。
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/.